Best 5 Statistical Paradoxes Data Scientists Should Know

Best 5 Statistical Paradoxes Data Scientists Should Know

Knowing these 5 statistical paradoxes is essential for data scientists to improve their analyses and machine learning models.

Introduction

As data scientists, we rely on statistical analysis to crawl information from the data about the relationships between different variables to answer questions, which will help businesses and individuals to make the right decisions. However, some statistical phenomena can be counterintuitive, possibly leading to paradoxes and biases in our analysis, which will ruin our analysis.

These paradoxes I will explain to you are easy to understand and do not include complex formulas. 

In this article, we will explore 5 statistical paradoxes data scientists should be aware of: the accuracy paradox, the False Positive Paradox, Gambler’s Fallacy, Simpson’s Paradox, and Berkson’s paradox.

Each of these paradoxes may be the potential reason for getting the unreliable result of your analysis.

Image by Author

We will discuss the definitions of these paradoxes and real-life examples to illustrate how these paradoxes can happen in real-world data analysis. Understanding these paradoxes will help you remove possible roadblocks to reliable statistical analysis.

So, without further ado, let’s dive into the world of paradoxes with Accuracy Paradox.

Accuracy Paradox

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Accuracy shows that accuracy is not a good evaluation metric when it comes to classifying.

Suppose you are analyzing a dataset that contains 1000 patient metrics. You want to catch a rare kind of disease, which will eventually be shown itself in 5% of the population. So overall, you have to find 50 people in 1000.

Even if you always say that the people do not have a disease, your accuracy will be 95%. And your model can't catch a single sick person in this cluster. (0/50)

Digits Data Set

Let’s explain this by giving an example from well-known digits data set.

This data set contains hand-written numbers from 0 to 9.

Image by Author 

It is a simple multilabel classification task, but it can also be interpreted as image recognition since the numbers are presented as images.

Now we will load these data sets and reshape the data set to apply the machine learning model. I am skipping explaining these parts because you might also be familiar with this part. If not, try searching digit data set or MNIST data set. MNIST data set also contains the same kind of data, but the shape is bigger than this one.

Alright, let’s continue.

Now we try to predict if the number is 6 or not. To do that, we will define a classifier that predicts not 6. Let’s look at the cross-validation score of this classifier.

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.base import BaseEstimator
import numpy as np

digits = datasets.load_digits()
n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1))
x_train, x_test, y_train, y_test = train_test_split(
    data, digits.target, test_size=0.5, shuffle=False
)
y_train_6 = y_train == 6

from sklearn.base import BaseEstimator


class DumbClassifier(BaseEstimator):
    def fit(self, X, y=None):
        pass

    def predict(self, X):
        return np.zeros((len(X), 1), dtype=bool)


dumb_clf = DumbClassifier()

cross_val_score(dumb_clf, x_train, y_train_6, cv=3, scoring="accuracy")

Here the results will be as the following.
 
 

What does it mean? That means even if you create an estimator that will never estimate 6 and you put that in your model, the accuracy can be over 90%. Why? Because 9 other numbers exist in our dataset. So if you say the number is not 6, you will be right 9/10 times.

This shows it’s important to choose your evaluation metrics carefully. Accuracy is not a good choice if you want to evaluate your classification tasks. You should choose precision or recall.

What are those? They come up in the False Positive Paradox, so continue reading.

False Positive Paradox

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Now, the false positive paradox is a statistical phenomenon that can occur when we test for the presence of a rare event or condition.

It is also known as the “base rate fallacy” or “base rate neglect”.

This paradox means there are more false positive results than positive results when testing rare events.

Let’s look at the example from Data Science.

Fraud Detection

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Imagine you are working on an ML model to detect fraudulent credit card transactions. The dataset you are working with includes a large number of normal (non-fraudulent) transactions and a small number of fraudulent transactions. Yet when you deploy your model in the real world, you find that it produces a large number of false positives.

After further investigation, you realize that the prevalence of fraudulent transactions in the real world is much lower than in the training dataset.

Let’s say 1/10,000 transactions will be fraudulent, and suppose the test also has a 5% rate of false positives.

TP = 1 out of 10,000

FP = 10,000*(100-40)/100*0,05 = 499,95 out of 9,999

So when a fraudulent transaction is found, what is the possibility that it really is a fraudulent transaction?

P = 1/500,95 =0,001996

The result is nearly 0.2%. It means when the event gets flagged as fraudulent, there is only a 0.2% probability that it really is a fraudulent event.

And that is a false positive paradox.

Here is how to implement it in Python code.

import pandas as pd
import numpy as np

# Number of normal transactions
normal_count = 9999

# Number of fraudulent transactions
true_positive = 1

# Number of normal transactions flagged as fraudulent by the model
false_positives = 499.95

# Number of fraudulent transactions flagged as normal by the model
false_negatives = 0

# Calculate precision
precision = (true_positive) / true_positive + false_positives
print(f"Precision: {precision:.2f}")

# Calculate recall
recall = (fraud_count) / fraud_count + false_negatives
print(f"Recall: {recall:.2f}")

# Calculate accuracy
accuracy = (
    normal_count - false_positives + fraud_count - false_negatives
) / (normal_count + fraud_count)
print(f"Accuracy: {accuracy:.2f}")

You can see that the recall is really high, yet the precision is very low.

To understand why systems do that, let me explain the precision/recall and precision/recall tradeoff.

Recall (true positive rate) is also called sensitivity. You should first find the positives and find the rate of true positives among them.

Recall = TP / TP + FP

Precision is the accuracy of positive prediction.

Precision = TP / TP + FN

Let’s say you want a classifier that will do sentiment analysis and predict whether the comments will be positive or negative. You might want a classifier that has high recall (it correctly identifies a high percentage of positive or negative comments). However, to have a higher recall, you should be okay with having a lower precision (misclassification of positive comments) because it is more important to delete negative comments than delete a few positive comments occasionally.

On the other hand, if you want to build a spam classifier, you might want a classifier that has high precision. It correctly identifies high percentages of spam, yet once in a while, it allows spam because it is more important to keep important mail.

Now in our case, to find a fraudulent transaction, you sacrifice getting many errors that are not fraudulent, yet if you do so, you have to take precautions, too, like in banking systems. When they detect fraudulent transactions, they begin to do further investigations to be absolutely sure. 

Typically they send a message to your phone or email for further approval when doing a transaction over a preset limit, etc.

If you allow your model to have a False negative, then your recall will be law. Yet, if you allow your model to have a False positive, your Precision will be low.

As a data scientist, you should adjust your model or add a step to make further investigations because there might be a lot of  False Positives.

Gambler’s Fallacy

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Gambler’s fallacy, also known as the Monte Carlo fallacy, is the mistaken belief that if an event happens more frequently than its normal probability, it will happen more often in the following trials.

Let’s look at the example from the Data Science field.

Customer Churn

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Imagine that you are building a machine learning model to predict whether the customer will churn based on their past behavior.

Now, you collected many different types of data, including the number of customers interacting with the services, the length of time they have been a customer, the number of complaints they have made, and more.

At this point, you can be tempted to think a customer who has been with the service for a long time is less likely to churn because they have shown a commitment to the service in the past.

However, this is an example of a gambler’s fallacy because the probability of a customer churning is not influenced by the length of time they have been a customer.

The probability of churn is determined by a wide range of factors, including the quality of the service, the customer's satisfaction with the service, and more of these factors.

So if you build a machine learning model, be careful explicitly not to create a column that includes the length of a customer and try to explain the model by using that. At this point, you should realize that this might ruin your model due to Gambler’s fallacy.

Now, this was a conceptual example. Let’s try to explain this by giving an example of the coin toss.

Let’s first look at the changes in the coin toss probability. You might be tempted to think that if the coin has come up heads several times, the possibility in the future will diminish. This is actually a great example of the gambler’s fallacy.

As you can see, in the beginning, the possibility fluctuated. Yet when the number of flips increases, the possibility of getting heads will converge to 0.5.

import random
import matplotlib.pyplot as plt

# Set up the plot
plt.xlabel("Flip Number")
plt.ylabel("Probability of Heads")

# Initialize variables
num_flips = 1000
num_heads = 0
probabilities = []

# Simulate the coin flips
for i in range(num_flips):
    if (
        random.random() > 0.5
    ):  # random() generates a random float between 0 and 1
        num_heads += 1
    probability = num_heads / (i + 1)  # Calculate the probability of heads
    probabilities.append(probability)  # Record the probability
# Plot the results
plt.plot(probabilities)
plt.show()

Now, let’s see the output.

Image by Author

It is obvious that probability fluctuates over time, but as a result, it will converge toward 0.5.

This example shows Gambler’s fallacy because the results of previous flips do not influence the probability of getting heads on any given flip. The probability remains fixed at 50% regardless of what has happened in the past.

Simpsons Paradox

Image by Roland Steinmann from Pixabay

This paradox happens when the relationship between two variables appears to change when data is aggregated.

Now, to explain this paradox, let’s use the built-in data set in seaborn, tips.

Tips

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To explain Simpson’s paradox, we will calculate the mean of the average tips women and men made during lunch and overall by using the tips data set. The tips dataset contains data on tips given by customers at a restaurant, like total tips, sex, day, time, and more.

The tips dataset is a collection of data on tips given by customers at a restaurant. It includes information such as the tip amount, the gender of the customer, the day of the week, and the time of day. The dataset can be used to analyze customers' tipping behavior and identify trends in the data.

import seaborn as sns

# Load the tips dataset
tips = sns.load_dataset("tips")

# Calculate the tip percentage for men and women at lunch
men_lunch_tip_pct = (
    tips[(tips["sex"] == "Male") & (tips["time"] == "Lunch")]["tip"].mean()
    / tips[(tips["sex"] == "Male") & (tips["time"] == "Lunch")][
        "total_bill"
    ].mean()
)
women_lunch_tip_pct = (
    tips[(tips["sex"] == "Female") & (tips["time"] == "Lunch")]["tip"].mean()
    / tips[(tips["sex"] == "Female") & (tips["time"] == "Lunch")][
        "total_bill"
    ].mean()
)

# Calculate the overall tip percentage for men and women
men_tip_pct = (
    tips[tips["sex"] == "Male"]["tip"].mean()
    / tips[tips["sex"] == "Male"]["total_bill"].mean()
)
women_tip_pct = (
    tips[tips["sex"] == "Female"]["tip"].mean()
    / tips[tips["sex"] == "Female"]["total_bill"].mean()
)

# Create a data frame with the average tip percentages
data = {
    "Lunch": [men_lunch_tip_pct, women_lunch_tip_pct],
    "Overall": [men_tip_pct, women_tip_pct],
}
index = ["Men", "Women"]
df = pd.DataFrame(data, index=index)
df

Alright, here is our data frame. 
 

 
As we can see, the average tip is bigger when it comes to lunch between men and women. Yet when data is aggregated, the mean is changed.

Let’s see the bar chart to see the changes.

import matplotlib.pyplot as plt

# Set the group labels
labels = ["Lunch", "Overall"]

# Set the bar heights
men_heights = [men_lunch_tip_pct, men_tip_pct]
women_heights = [women_lunch_tip_pct, women_tip_pct]

# Create a figure with two subplots
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))

# Create the bar plot
ax1.bar(labels, men_heights, width=0.5, label="Men")
ax1.bar(labels, women_heights, width=0.3, label="Women")
ax1.set_title("Average Tip Percentage by Gender (Bar Plot)")
ax1.set_xlabel("Group")
ax1.set_ylabel("Average Tip Percentage")
ax1.legend()

# Create the line plot
ax2.plot(labels, men_heights, label="Men")
ax2.plot(labels, women_heights, label="Women")
ax2.set_title("Average Tip Percentage by Gender (Line Plot)")
ax2.set_xlabel("Group")
ax2.set_ylabel("Average Tip Percentage")
ax2.legend()

# Show the plot
plt.show()

Here is the output.

Image by Author

Now, as you can see, the average changes as data are aggregated. Suddenly, you have data showing that overall, women tip more than men.

What is the catch?

When observing the trend from the subset version and extracting meaning from them, be careful not to forget to check whether this trend is still the case for the whole data set or not. Because as you can see, there might not be the case in special circumstances. This can lead a Data Scientist to make a misjudgment, leading to a poor (business) decision.

Berkson’s Paradox

Berkson’s Paradox is a statistical paradox that happens when two variables correlated to each other in data, yet when the data will subsetted, or grouped, this correlation is not observed & changed.

In simple terms, Berkson's Paradox is when a correlation appears to be different in different subgroups of the data.

Now let’s look into it by analyzing the Iris dataset.

Iris Data set

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The Iris dataset is a commonly used dataset in machine learning and statistics. It contains data for different observations of irises, including their petal and sepal length and width and the flower species observed.

Here, we will draw two graphs showing the relationship between sepal length and width. But in the second graph, we filter the species as a setosa.

import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import linregress

# Load the iris data set
df = sns.load_dataset("iris")

# Subset the data to only include setosa species
df_s = df[df["species"] == "setosa"]

# Create a figure with two subplots
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))

# Plot the relationship between sepal length and width.
slope, intercept, r_value, p_value, std_err = linregress(
    df["sepal_length"], df["sepal_width"]
)
ax1.scatter(df["sepal_length"], df["sepal_width"])
ax1.plot(
    df["sepal_length"],
    intercept + slope * df["sepal_length"],
    "r",
    label="fitted line",
)
ax1.set_xlabel("Sepal Length")
ax1.set_ylabel("Sepal Width")
ax1.set_title("Sepal Length and Width")
ax1.legend([f"R^2 = {r_value:.3f}"])

# Plot the relationship between setosa sepal length and width for setosa.
slope, intercept, r_value, p_value, std_err = linregress(
    df_s["sepal_length"], df_s["sepal_width"]
)
ax2.scatter(df_s["sepal_length"], df_s["sepal_width"])
ax2.plot(
    df_s["sepal_length"],
    intercept + slope * df_s["sepal_length"],
    "r",
    label="fitted line",
)
ax2.set_xlabel("Setosa Sepal Length")
ax2.set_ylabel("Setosa Sepal Width")
ax2.set_title("Setosa Sepal Length and Width ")
ax2.legend([f"R^2 = {r_value:.3f}"])

# Show the plot
plt.show()

You can see the changes between sepal length and within the setosa species. Actually, it shows a different correlation than other species.
 

Image by Author

Also, you can see that setosa’s different correlation in the first graph.

In the second graph, you can see that the correlation between sepal width and sepal length has changed. When analyzing all data set, it shows that when sepal length increases, sepal width decreases. However, if we start analyzing by selecting setosa species, the correlation is now positive and shows that when sepal width increases, sepal length increases as well.

import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import linregress

# Load the tips data set
df = sns.load_dataset("iris")

# Subset the data to only include setosa species
df_s = df[df["species"] == "setosa"]

# Create a figure with two subplots
fig, ax1 = plt.subplots(figsize=(5, 5))

# Plot the relationship between sepal length and width.
slope, intercept, r_value_1, p_value, std_err = linregress(
    df["sepal_length"], df["sepal_width"]
)
ax1.scatter(df["sepal_length"], df["sepal_width"], color="blue")
ax1.plot(
    df["sepal_length"],
    intercept + slope * df["sepal_length"],
    "b",
    label="fitted line",
)

# Plot the relationship between setosa sepal length and width for setosa.
slope, intercept, r_value_2, p_value, std_err = linregress(
    df_s["sepal_length"], df_s["sepal_width"]
)
ax1.scatter(df_s["sepal_length"], df_s["sepal_width"], color="red")
ax1.plot(
    df_s["sepal_length"],
    intercept + slope * df_s["sepal_length"],
    "r",
    label="fitted line",
)

ax1.set_xlabel("Sepal Length")
ax1.set_ylabel("Sepal Width")
ax1.set_title("Sepal Length and Width")
ax1.legend([f"R = {r_value_1:.3f}"])

Here is the graph. 
 

Image by Author

You can see that starting by analyzing with setosa and generalizing the sepal width and length correlation will lead you to make a false statement according to your analysis.

Conclusion

In this article, we examined five statistical paradoxes that data scientists should be aware of in order to do accurate analysis. Let’s suppose you think that you found a trend in your data set, which indicates that when sepal length increases, sepal width increases as well. Yet when looking at the whole data set, it is actually the total opposite.

Or you might be assessing your classification models by looking at the accuracy. You see that even the model that does nothing can achieve over 90% accuracy. If you tried to evaluate your model with accuracy and do analysis accordingly, think about how many miscalculations you can make.

By understanding these paradoxes, we can take steps to avoid common pitfalls and improve the reliability of our statistical analysis. It’s also good to approach data analysis with a healthy dose of skepticism and avoid potential paradoxes and limitations in your analyses.

In conclusion, these paradoxes are important for Data Scientists when it comes to high-level analysis, as being aware of them can improve the accuracy and reliability of our analysis. We also recommend this “Statistics Cheat Sheet” that can help you understand the important terms and equations for statistics and probability and can help you for your next data science interview.

Thanks for reading!

Original article source at: https://www.kdnuggets.com/

#datascientist #statistical 

Best 10 Ways How ChatGPT Can Help Data Scientists Enhance Their Work

Best 10 Ways How ChatGPT Can Help Data Scientists Enhance Their Work

Introduction

Data scientists face a wide range of challenges in their work, from managing large data sets to developing complex models that accurately predict outcomes. With so many tasks to handle, data scientists can benefit greatly from the assistance of advanced tools and technologies. ChatGPT, the powerful language model developed by OpenAI, offers a range of capabilities that can help data scientists enhance their work in numerous ways. In this article, we’ll explore 10 ways ChatGPT can assist data scientists in their work.

Improving Data Collection and Pre-processing

One of the most time-consuming aspects of data science work is collecting and pre-processing data. ChatGPT can assist data scientists by providing automated tools for collecting and cleaning data, reducing the time and effort needed for these tasks.

Simplifying Data Exploration

Data exploration is an essential part of data science work, but it can be challenging to analyze large datasets effectively. ChatGPT can help data scientists simplify data exploration by providing natural language processing capabilities that allow them to ask questions and receive relevant insights quickly.

Streamlining Model Building

Model building is another critical aspect of data science work, and ChatGPT can help by providing automated tools for model building and testing. This can reduce the time and effort required to build effective models and improve the accuracy of the resulting predictions.

Enhancing Natural Language Processing

Natural language processing (NLP) is a crucial component of many data science projects, and ChatGPT can help data scientists enhance their NLP capabilities. With ChatGPT, data scientists can build advanced NLP models that can analyze and process text data more efficiently.

Improving Data Visualization

Data visualization is an important tool for communicating complex data insights effectively, and ChatGPT can help data scientists create compelling visualizations quickly and easily. By using ChatGPT’s natural language capabilities, data scientists can generate charts and graphs that highlight key insights in their data.

Providing Automated Reports and Insights

ChatGPT can help data scientists generate automated reports and insights that summarize their findings quickly and easily. This can save time and effort and help data scientists communicate their findings more effectively to others.

Enhancing Data Security

Data security is a critical concern for data scientists, and ChatGPT can help by providing advanced security features that protect sensitive data from unauthorized access.

Improving Workflow Efficiency

ChatGPT can help data scientists improve their workflow efficiency by automating repetitive tasks and streamlining the overall data science process. This can help data scientists focus on more critical tasks and achieve better results.

Supporting Collaborative Work

Data science projects often involve collaboration between multiple team members, and ChatGPT can help facilitate this collaboration by providing tools for shared data access and analysis.

Offering Continuous Learning and Improvement

Finally, ChatGPT can help data scientists achieve continuous learning and improvement by providing access to the latest data science techniques and technologies. With ChatGPT, data scientists can stay up to date on the latest trends and best practices in the field.

Conclusion

Data science work can be challenging, but with the assistance of advanced tools like ChatGPT, data scientists can achieve better results more efficiently. By leveraging ChatGPT’s capabilities for data collection, processing, analysis, and reporting, data scientists can enhance their workflow efficiency, improve their model building, and achieve

Original article source at: https://www.cloudbooklet.com/

#chatgpt #datascientist 

Top 10 Skills To Master for Becoming A Data Scientist

Top 10 Skills To Master for Becoming A Data Scientist

How To Become A Data Scientist?

This blog is a guide on how to become a Data Scientist. One thing is for sure, you cannot become a data scientist overnight. It’s a journey, for sure and a challenging one.

I am assuming that you are a fresher, so if you are planning to begin your career in Data Science, there is a protracted sojourn.

But how do I go about becoming one?

Where should I start from?

What is my learning roadmap?

Which tools and techniques do I need to know?

How will I know when I have achieved my goal?

You may also go through this recording of “how to become a data scientist” where you can understand the topics in a detailed manner.

In this post, I will address all of these questions.

I have listed down all the skills required to become a Data Scientist:

1. Fundamentals
2. Statistics
3. Programming
4. Machine Learning and Advanced Machine Learning (Deep Learning)
5. Data Visualization
6. Big Data
7. Data Ingestion
8. Data Munging
9. Tool Box
10. Data-Driven Problem Solving

Once you acquire these skills, Congratulations! You are a Data Scientist.

Below is the road map for becoming a Data Scientist.

Probably it took 5 minutes to read this post on how to become a Data Scientist, but yeah, be prepared for a long hectic journey in becoming one.

Now, let me explain all of these skills one by one. I hope that will make this blog more useful :)

Fundamentals:

This includes:

• Matrices and Linear Algebra Functions
• Hash Functions and Binary Tree
• Relational Algebra, Database Basics
• ETL ( Extract Transform Load )
• Reporting VS BI (Business Intelligence) VS Analytics

Statistics:

This includes:

• Descriptive Statistics (Mean, Median, Range, Standard Deviation, Variance)
• Exploratory Data Analysis
• Percentiles and Outliers
• Probability Theory
• Bayes Theorem
• Random Variables
• Cumulative Distribution function (CDF)
• Skewness
• Other Statistics fundamentals

I would suggest you to pick a dataset from UCI repo. and start right now!

Programming:

Expertise in any one programming language, I would suggest ‘R’ or ‘Python.

Machine Learning and Advanced Machine Learning (Deep Learning):

You should understand what is Machine learning and how it works.

Understand different types of Machine Learning techniques:

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

Good knowledge on various Supervised and Unsupervised learning algorithms is required such as:

• Linear Regression
• Logistic Regression
• Decision Tree
• Random Forest
• K Nearest Neighbor
• Clustering (for example K-means)

Nowadays everyone is talking about Deep Learning, as it solved a lot of limitations of traditional Machine Learning approaches. I would suggest you to understand how Deep Learning works. I have listed down few Deep Learning concepts that you should be familiar with:

• Fundamentals of Neural Networks
• Any one library used for creating Deep Learning models, such as Tensorflow or Keras.
• Understand how Convolutional Neural Networks, Recurrent Neural Networks and RBM and Autoencoders work.

Data Visualization:

Data visualization is a very important part of Data life-cycle. 

Good hands-on knowledge is required on various visualization tools. Even, you can use a programming language for that purpose.

Below are few visualization tools:

• Tableau
• Kibana
• Google Charts
• Datawrapper

Big Data:

Big Data is everywhere and there is almost an urgent need to collect and preserve whatever data is being generated, for the fear of missing out on something important.

There is a huge amount of data floating around. What we do with it is all that matters right now. This is why Big Data Analytics is in the frontiers of IT. Big Data Analytics has become crucial as it aids in improving business, decision makings and providing the biggest edge over the competitors. This applies for organizations as well as professionals in the Analytics domain.

As a Data Scientist it is very important to have knowledge about frameworks that can process Big Data. Two of the most famous ones are ‘Hadoop’ and ‘Spark’.

Data Ingestion:

The process of importing , transferring , loading and processing data for later use or storage in a database is called Data Ingestion. This involves loading data from a variety of sources.

Below are few Data Ingestion tools:

• Apache Flume
• Apache Sqoop

Data Munging:

If you have ever performed data analysis, you might have come across feature selection before you apply your Analytical model to the data.

So, in general, all the activity that you do on the raw data to make it “clean” enough to input to your analytical algorithm is data munging.

You can use ‘R’ and ‘Python’ packages for that.

It is one of the most important part of the data life-cycle.

As a Data Scientist you should be able to understand what all features are important in the dataset and what all features can be removed. You should also be able to identify your dependent variable or label. 

Obviously, you have to remove inconsistency in the dataset.

All of these things are part of Data Munging (Data Wrangling).

Tool Box:

You might find this section pretty redundant, but I think it is very very important to have good knowledge on certain tools like:

• MS Excel
• Python or R
• Hadoop
• Spark
• Tableau

Data-Driven Problem Solving:

All the things we have discussed so far, includes tools and technologies that you can learn. But, Data-Driven problem solving approach is something that you need to develop. It will only come with experience.

A Data Scientist needs to know how to productively approach a problem.

This means identifying a situation’s

• salient features,
• figuring out how to frame a question that will yield the desired answer,
• deciding what approximations make sense, and
• consulting the right co-workers at the appropriate junctures of the analytic process.

All of that in addition to knowing which data science methods to apply to the problem at hand.

I think I have pretty much covered everything. I hope you found this blog useful.

All the best for your journey in becoming a Data Scientist.

How to Become a Data Scientist | Data Scientist Skills

This video will explain all the skills required for becoming a modern day Data Scientist.

Original article source at: https://www.edureka.co/ 

#datascientist #skills 

Your Guide To Unlocking Top Data Scientist Jobs

Data Science Career Opportunities: Your Guide To Unlocking Top Data Scientist Jobs

In a world where 2.5 quintillion bytes of data is produced every day, a professional who can organize this humongous data to provide business solutions is indeed the hero! Much has been spoken about why Big Data is here to stay and why Big Data Analytics is the best career move. Building on what’s already been written and said, let’s discuss Data Science career opportunities and why ‘Data Scientist’ is the sexiest job title of the 21st century.

Data Science Career Opportunities

A Data Scientist, according to Harvard Business Review, “is a high-ranking professional with the training and curiosity to make discoveries in the world of Big Data”. Therefore it comes as no surprise that Data Scientists are coveted professionals in the Big Data Analytics and IT industry.

With experts predicting that 40 zettabytes of data will be in existence by 2020 (Source), Data Science career opportunities will only shoot through the roof! Shortage of skilled professionals in a world that is increasingly turning to data for decision-making has also led to the huge demand for Data Scientists in start-ups and well-established companies. A McKinsey Global Institute study states that by 2018, the US alone will face a shortage of about 190,000 professionals with deep analytical skills. With the Big Data wave showing no signs of slowing down, there’s a rush among global companies to hire Data Scientists to tame their business-critical Big Data.

Data Scientist Salary Trends

A report by Glassdoor shows that Data scientists lead the pack for the best jobs in America. The report goes on to say that the median salary for a Data Scientist is an impressive $91,470 in the US and ₹622,162 and there are over 2300 job openings posted on the site (Source).

On Indeed.com, the average Data Scientist salaries for job postings in the US are 80% higher than average salaries for all job postings nationwide, as of May 2019.

In India the trend is no different; as of May 2019,  the median salary for a Data Scientist role is Rs. 622,162 according to Payscale.com.

Data Scientist Job Roles

A Data Scientist dons many hats in his/her workplace. Not only are Data Scientists responsible for business analytics, they are also involved in building data products and software platforms, along with developing visualizations and machine learning algorithms.

Some of the prominent Data Scientist job titles are:

• Data Scientist
• Data Architect
• Data Administrator
• Data Analyst
• Business Analyst
• Data/Analytics Manager
• Business Intelligence Manager

TOP DATA SCIENCE: PROFILES

Hot Data Science Skills

Coding skills clubbed with knowledge of statistics and the ability to think critically, make up the arsenal of a successful data scientist. Some of the in-demand Data Scientist skills that will fetch big career opportunities in Data Science are:

• Programming Languages: R/Python/Java
• Statistics and Applied Mathematics
• Working Knowledge of Hadoop and Spark
• Databases: SQL and NoSQL
• Machine Learning and Neural Networks
• Proficiency in Deep Learning Frameworks: TensorFlow, Keras, Pytorch
• Creative Thinking & Industry Knowledge

The Payscale.com chart below shows the average Data Scientist Salary by skills in the USA and India.

Currency: India – ₹, US – $

The upward swing in Data Science career opportunities is expected to continue for a long time to come. As data pervades our life and companies try to make sense of the data generated, skilled Data Scientists will be continued to be wooed by businesses big and small. Case in point, a look at the jobs board on Indeed.com reveals top companies competing with each other to hire Data Scientists. A few big names include Facebook, Twitter, Airbnb, Apple, LinkedIn, IBM, and PayPal among others.

The time is ripe to up-skill in Data Science and Big Data Analytics to take advantage of the Data Science career opportunities that come your way. This is the best opportunity to kick off your career in the field of data science by taking the Data Science Training.

Also, Edureka has a specially curated Data Science with Python course which helps you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes. You’ll also learn the concepts of Statistics, Time Series, Text Mining, and an introduction to Deep Learning. New batches for the Data Science course are starting soon!!

Got a question for us? Please mention it in the comments section and we will get back to you.

Original article source at: https://www.edureka.co/

#datascientist #jobs #datascience 

Difference Between Data Analyst and Data Scientist

Data Analyst vs Data Scientist: What's the Difference? Data scientist and data analyst are both in-demand career paths you can follow in big data. 

Data analyst and data scientist are two career paths in big data. And while they do have similarities, each requires different skills.

The basic difference between the two is that a data scientist works to capture data while a data analyst tries to gain insights from that data.

This article is for you if you’re interested in a career in big data and you don’t know whether you'd want to be a data analyst or data scientist. It will also help you if you just want to know the differences between a data analyst and a data scientist.

What We'll Cover

• What is Data Analytics and Who is a Data Analyst?
  • What does a Data Analyst Do?
  • How to Become a Data Analyst
• What is Data Science and Who is a Data Scientist?
  • What does a Data Scientist Do?
  • How to Become a Data Scientist
• What are the Differences between Data Analyst and Data Scientist?
• Conclusion

What is Data Analytics and Who is a Data Analyst?

Data analytics bridges the gap between data science and business analytics. It is the systematic approach of processing raw data and subsequently extracting meaningful information from it.

The information extracted from the raw data is the focus of data analysis. The professional who does this analysis is a data analyst.

What does a Data Analyst Do?

Data analysts make use of statistical and logical techniques to evaluate data. They use tools such as SQL to query databases and extract the needed information that can help companies make better decisions.

To dig into and assess the information from this data, a data analyst uses programming languages like R, SAS, and Python, and tools like D3, Tableau, and Power BI.

In addition, a data analyst cleans up the database by getting rid of redundant and unusable data.

How to Become a Data Analyst

To become a data analyst, you can earn a relevant degree from an accredited college or university, attend a bootcamp, or learn it yourself.

You can learn to become a data analyst yourself because building a career in a certain field in tech is all about skills. Once you have those skills and you can put them into practical use, then you can become a data analyst.

Some job requirements for data analysts include degrees and some don’t. So there’s room for anyone who doesn’t have a degree but has the skills.

As a data analyst, the skills you need are:

• Soft skills (critical thinking, communication, and others)
• Data visualization (D3, Tableau, Power BI)
• SQL and (probably) NoSQL
• Statistics
• Spreadsheets (Excel, Google Sheets, and others)
• A few programming languages like Python, R, SAS, and JavaScript for D3
• Machine learning

It doesn’t end there. You should try to work on projects that make you appear employable to recruiters. You should also try to get an entry-level job that can help you put those skills into real-world practice. And if you can’t find an entry-level job, then you can consider volunteering.

Here are a few resources you can use to get started:

1. Learn Data Analysis with Python
2. What is Data Analysis? Full Handbook
3. Data Analysis with Python for Excel Users
4. What does a Data Analyst Do?

What is Data Science and Who is a Data Scientist?

Data science is the development of strategies for capturing data and preparing it for analysis. It also involves processing and developing data models with programming languages like R and Python, then deploying those models into applications. The professional who develops these strategies is called a data scientist.

What does a Data Scientist Do?

A data scientist is more focused on developing and implementing tools that help data analysts analyze the data and extract the needed information from it.

This means data scientists spend their time developing models and preparing algorithms. And if the organization needs to deploy a model, data scientists are in charge of that.

How to Become a Data Scientist

Most data science job openings require a relevant degree such as Statistics and Computer Science. But on a personal note, I’ve seen data science openings that don’t require degrees.

Towards the end of this article, I will link an article that shows you where to see those data science job openings.

Once again, what matters is the skills. Once you have those skills and can put them into use, then you can get a job as a data scientist.

Some of the skills you need to become a data scientist are:

• Mathematics
• Programming (Python, R, SAS)
• Statistics
• Linear algebra
• Machine learning
• Cloud computing
• SQL and NoSQL (Most openings won’t require NoSQL but it’s a good skill to learn)
• Apache Hadoop
• Calculus

Here are some resources to get you started:

1. Learn the Basics of Data Science - Hands-On Course
2. Python for Data Science Course
3. Top Statistics Concepts to Know Before Getting Into Data Science
4. Data Science Interview Questions for Beginners
5. Programming, Math, and Science Concepts to Know for Data Science

What are the Differences between Data Analyst and Data Scientist?

Conclusion

Data scientist and data analyst are both in-demand career paths you can follow in big data. If you’re confused about which to take get into between the two, here are some things to consider:

• if you’re well-versed in Mathematics, Statistics, and computer science, either of the two is good for you
• if you want to create advanced machine learning models, you should consider getting into data science
• if you are interested in analytics, you’d probably make a great data analyst.

There’s no black-and-white guide to help you choose between becoming a data scientist and a data analyst. And it's not helpful to say one is better than the other.

In the end, what matters is solving problems and helping humanity learn and improve, not how much a data analyst makes or how much a data scientist makes.

Thank you for reading.

Original article source at https://www.freecodecamp.org

#dataanalyst #datascientist

HOW CAN A FRESHER ENTER THE FIELD OF DATA SCIENCE?

Industries such as BFSI (Banking, Financial Services, and Insurance), Energy, Pharmaceuticals, and Electronic Commerce are just a few examples where the need for data scientists is growing rapidly. Read more

#datascience #data-analysis #datascientist  #dataengineering #pandas #python #data 

7 Façons De Gagner 2 000 $/mois En Tant Que Data Scientist Indépendant

Aujourd'hui, je vais vous dire exactement comment vous pouvez gagner du support et en tant que spécialiste de la science des données, je pense que la recherche d'un emploi devient très difficile lorsque vous faites face à de nombreux rejets.

Ce n'est certainement pas un clickbait, je vais vous guider et vous aider à rassembler les informations nécessaires sur la façon dont vous pouvez gagner en tant que data scientist indépendant.

Avis de non-responsabilité : les informations fournies ici sont destinées aux personnes qui n'abandonneront pas et je n'assume aucune responsabilité si vous n'êtes pas en mesure de gagner beaucoup. Un conseil important pour tous mes collègues lecteurs est que vous devez être patient, cohérent et avoir un plan. Avant de suivre l'un des plans ci-dessous, veuillez effectuer des recherches à ce sujet, puis prendre des mesures.

Commençons.

1] Travailler en tant que consultant en données

Il existe de nombreux espaces de travail relâchés où vous pouvez obtenir des centaines de clients, il vous suffit de savoir à qui vous pouvez présenter et quelle entreprise vous propose la meilleure offre. Au départ, vous pouvez commencer avec des taux bas, puis progressivement avec une certaine expérience, vous pouvez ensuite augmenter les taux.

Il y a beaucoup d'étudiants à l'étranger au Royaume-Uni, au Canada, en Australie, aux États-Unis, etc. qui ont besoin d'aide pour leurs projets ou missions de science des données, vous pouvez offrir cette aide et vous pouvez en tirer profit.

Comment saurez-vous par où commencer?

Vous pouvez utiliser votre profil LinkedIn pour contacter des étudiants de divers collèges du monde entier et vous pouvez présenter vos idées. S'ils sont d'accord, vous pouvez commencer immédiatement.

Il y a tellement d'espaces de travail Slack que vous pouvez rejoindre et ces espaces de travail Slack sont entièrement dédiés à la science des données, à l'apprentissage automatique et à tout ce qui s'y rapporte.

2] Travailler comme

Moyen

écrivain

Créez des articles et continuez à publier sur le support, et une fois que vous êtes dans votre programme de partenariat moyen.

Vous ne gagnerez peut-être pas 1 000 $ du jour au lendemain. Vous pouvez publier dans des publications majeures et vous pouvez gagner un montant bon et décent. C'est quelque chose que vous pouvez prendre des mois pour grandir et construire lentement, vous pouvez également publier dans diverses publications sur Medium.

3] Créez votre propre bibliothèque/paquet

Vous pouvez créer votre propre bibliothèque/package et le publier sur PyPi. Et cela peut être rendu open source ou vous pouvez le gagner en le vendant au grand public.

Cette bibliothèque/paquet pourrait résoudre certains problèmes généraux auxquels tous les data scientists sont confrontés lors de la création de leurs modèles d'apprentissage automatique. Ce produit peut être destiné à un groupe spécifique de personnes ou peut être générique.

TensorFlow

, Kafka , PyTorch , sont tous des bibliothèques/paquets. Vous pouvez donc maintenant comprendre à quel point les bibliothèques et les packages sont importants. En outre, vous pouvez également constituer votre équipe et construire quelque chose ensemble. Cela vous facilite la vie, et celle de tous les autres.

Pour commencer, il n'y a pas beaucoup de bibliothèques qui offrent une automatisation sociale. Vous pouvez donc créer des choses comme ça et les vendre.

4] Créez et développez votre chaîne YouTube

C'est l'une des meilleures options pour grandir en tant que leader d'opinion, Krish Naik, Sentdex, et de nombreuses personnes sont devenues célèbres et leurs compétences pédagogiques sont superbes. Pour ce faire, vous avez juste besoin d'une petite quantité d'investissements pour la lumière et peut-être même un microphone.

Ces investissements peuvent être fructueux à long terme. Vous pouvez créer votre chaîne sur la base de la science des données, de l'apprentissage automatique, de l'analyse, des projets, tout peut être un sujet. Les vidéos "Comment faire" sont très courantes, vous pouvez enseigner de bonnes choses de bibliothèque, que vous avez créées, ou peut-être que vous avez apprises au fil du temps. Vous pouvez également vous procurer n'importe quel livre sur la science des données et fournir également des critiques.

Ces activités peuvent être réalisées une par une ou toutes en une seule fois, tout dépend de la manière dont vous souhaitez procéder.

Photo de Daniel Thomas sur Unsplash

5] Inscrivez-vous en tant que pigiste sur plusieurs plateformes

L'inscription sur ces plateformes vous donne un bon départ et au début, il peut être difficile d'être payé, mais vous devez être patient. Les choses fonctionnent, et elles fonctionnent également bien sur ces plateformes. Je suggérerais de commencer par Kolabtree , car il est inexploité.

Il vous offre de nombreuses options et vous pouvez voir que rien qu'en Data Science and Analysis, il y a plus de 100 offres d'emploi qui sont publiées chaque mois. Vous pouvez postuler pour tous si vous souhaitez le faire.

D'autres canaux tels que Upwork , PeoplePerHour , Freelancer , Fiverr , Outvise , Toptal , sont d'autres sites/plateformes de freelance pour les concerts liés à la science des données

6] Devenez un auteur de livre technique ou un rédacteur technique

Si vous êtes déjà un bon data scientist, vous pouvez écrire votre propre livre, en collaboration avec d'autres data scientists. Cela peut être vraiment frustrant et un processus à long terme. Je ne dis pas que cela vous apportera du succès du jour au lendemain, mais cela vous donnera un revenu passif à vie.

Vous pouvez consulter certains des livres magnifiquement écrits et faire partie des lectures classiques pour quelqu'un qui souhaite se lancer en tant que débutant dans le domaine de la science des données.

Encore une fois, c'est quelque chose qui dépend de la profondeur de votre réseau, donc je suggérerais en fait de commencer à construire votre réseau sur divers canaux de médias sociaux et de tirer parti de vos abonnés, puis de prendre cette étape audacieuse d'écrire votre livre.

7] Réseaux sociaux

J'ai vu de nombreuses personnes utiliser le puissant effet de levier des médias sociaux pour obtenir des avantages monétaires. Cela vaut pour tous les créateurs de contenu et les leaders d'opinion du monde.

Certains leaders d'opinion vraiment incroyables que vous devez suivre peuvent être trouvés ici:

21 professeurs leaders d'opinion en science des données (mastersindatascience.org)

Vous pouvez commencer à publier quotidiennement de nouveaux codes ou livres, ou quelque chose d'intéressant qui se passe dans l'industrie et vos réflexions sur certaines nouvelles versions de bibliothèque, tout ce qui concerne la science des données et développer votre audience sur n'importe quelle plate-forme.

Choisissez une plate-forme et respectez-la, pendant quelques mois, vous pouvez gagner de nombreuses pistes à de nombreuses fins, cela peut être pour un formateur en science des données dans certains instituts, ou il peut s'agir d'obtenir des révisions de code, ou vous pouvez participer à des événements payants. Votre croissance ne se limite pas à ces plates-formes, vous pouvez vous étendre à divers canaux et croître de manière exponentielle.

Je suggérerais de commencer par LinkedIn et de se connecter avec d'autres data scientists. Vous pouvez bien présenter votre profil et être un leader d'opinion dans le domaine. Vous pouvez générer une bonne quantité de prospects à partir des médias sociaux et être payé pour des conseils sur des éléments liés aux données.

Vous pouvez toujours avoir plus de façons de gagner et l'un des moyens les plus puissants de gagner est de créer du contenu éducatif sur des plateformes telles que Udemy, Coursera, etc. Vous pouvez combiner toutes les méthodes mentionnées ci-dessus, ou commencer par une ou deux et décider progressivement. sur la façon dont vous voulez procéder.

Lien : https://medium.com/faun/7-ways-to-earn-2000-month-as-a-freelance-data-scientist-d74ae4e73fd8

#datascientist

7 Cách để Kiếm $ 2000/Tháng Với Tư Cách Là Nhà Khoa Học Dữ Liệu tự do

Hôm nay tôi sẽ cho bạn biết chính xác cách bạn có thể kiếm tiền từ mức trung bình và với tư cách là một nhân viên khoa học dữ liệu cốt lõi, tôi tin rằng tìm kiếm một công việc sẽ rất khó khăn khi bạn phải đối mặt với rất nhiều lời từ chối.

Đây chắc chắn không phải là một chiêu dụ mà tôi sẽ hướng dẫn bạn và giúp bạn thu thập thông tin cần thiết về cách bạn có thể kiếm tiền với tư cách là một nhà khoa học dữ liệu tự do.

Tuyên bố từ chối trách nhiệm: Thông tin được cung cấp ở đây dành cho những người không bỏ cuộc và tôi không chịu bất kỳ trách nhiệm nào nếu bạn không thể kiếm được nhiều. Lời khuyên quan trọng cho tất cả các độc giả của tôi là bạn cần phải kiên nhẫn, nhất quán và nên có kế hoạch. Trước khi thực hiện bất kỳ kế hoạch nào dưới đây, vui lòng nghiên cứu về nó và sau đó thực hiện bất kỳ hành động nào.

Hãy để chúng tôi bắt đầu.

1] Làm tư vấn dữ liệu

Có rất nhiều không gian làm việc đơn giản, nơi bạn có thể có được hàng trăm khách hàng, bạn chỉ cần biết ai là người bạn có thể chào hàng và công ty nào cung cấp cho bạn thỏa thuận tốt nhất. Ban đầu, bạn có thể bắt đầu với tỷ lệ thấp và sau đó dần dần với một số kinh nghiệm, bạn có thể tăng tỷ lệ.

Có rất nhiều sinh viên ở nước ngoài ở Anh, Canada, Úc, Mỹ, v.v. cần trợ giúp với các dự án hoặc bài tập về khoa học dữ liệu của họ, bạn có thể cung cấp sự trợ giúp này và bạn có thể kiếm được tiền từ nó.

Làm thế nào bạn sẽ biết bắt đầu từ đâu?

Bạn có thể sử dụng hồ sơ LinkedIn của mình để tiếp cận với sinh viên từ các trường đại học khác nhau trên toàn cầu và bạn có thể giới thiệu ý tưởng của mình. Nếu họ đồng ý, bạn có thể bắt đầu ngay lập tức.

Có rất nhiều không gian làm việc Slack mà bạn có thể tham gia và những không gian làm việc slack này hoàn toàn dành riêng cho khoa học dữ liệu, máy học và những thứ liên quan đến nó.

2] Làm việc như một

Vừa phải

nhà văn

Tạo các bài báo và tiếp tục xuất bản trên phương tiện và khi bạn đã tham gia chương trình đối tác phương tiện của mình.

Bạn có thể không kiếm được $ 1000 qua đêm. Bạn có thể xuất bản lên các ấn phẩm lớn và bạn có thể kiếm được một khoản tiền kha khá. Đây là thứ mà bạn có thể mất hàng tháng để phát triển và xây dựng từ từ, bạn cũng có thể xuất bản trong nhiều ấn phẩm khác nhau trên Medium.

3] Tạo thư viện / gói của riêng bạn

Bạn có thể tạo thư viện / gói của riêng mình và xuất bản trên PyPi. Và điều này có thể được làm mã nguồn mở hoặc bạn có thể kiếm được nó bằng cách bán nó cho công chúng.

Thư viện / gói này có thể giải quyết một số vấn đề chung mà tất cả các nhà khoa học dữ liệu gặp phải khi tạo mô hình học máy của họ hoặc tương tự. Sản phẩm này có thể dành cho một nhóm người cụ thể hoặc có thể chung chung.

TensorFlow

, Kafka , PyTorch , đều là các thư viện / gói. Vì vậy, bây giờ bạn có thể hiểu thư viện và gói quan trọng như thế nào. Ngoài ra, bạn cũng có thể tìm ra nhóm của mình và cùng nhau xây dựng một cái gì đó. Về cơ bản, nó làm cho cuộc sống của bạn và mọi người dễ dàng hơn.

Đầu tiên, không có nhiều thư viện cung cấp tính năng tự động hóa xã hội. Vì vậy, bạn có thể tạo ra một cái gì đó tương tự và bán chúng.

4] Xây dựng và phát triển kênh YouTube của bạn

Đây là một trong những lựa chọn tốt nhất để phát triển như một nhà lãnh đạo tư tưởng, Krish Naik, Sentdex, và nhiều người đã trở nên nổi tiếng và kỹ năng giảng dạy của họ là tuyệt vời. Để làm được điều này, bạn chỉ cần một khoản đầu tư nhỏ cho ánh sáng và có thể là cả một chiếc micrô.

Những khoản đầu tư này có thể mang lại hiệu quả về lâu dài. Bạn có thể xây dựng kênh của mình dựa trên khoa học dữ liệu, máy học, phân tích, dự án, bất cứ điều gì có thể là một chủ đề. Video “Cách thực hiện” rất phổ biến, bạn có thể dạy một số nội dung thư viện hay mà bạn đã tạo ra hoặc có thể bạn đã học được theo thời gian. Bạn cũng có thể chọn bất kỳ cuốn sách nào về khoa học dữ liệu và cung cấp các bài đánh giá.

Những hoạt động này có thể được thực hiện từng cái một hoặc tất cả trong một lần, hoàn toàn phụ thuộc vào cách bạn muốn tiến hành.

Ảnh của Daniel Thomas trên Unsplash

5] Đăng ký làm freelancer trên nhiều Nền tảng

Đăng ký trên các nền tảng này mang lại cho bạn một khởi đầu tuyệt vời và ban đầu bạn có thể khó được trả tiền, nhưng bạn phải kiên nhẫn. Mọi thứ diễn ra suôn sẻ và chúng cũng hoạt động tốt trên các nền tảng này. Tôi khuyên bạn nên bắt đầu với Kolabtree , vì nó chưa được khai thác.

Nó cung cấp cho bạn nhiều tùy chọn và bạn có thể thấy rằng chỉ trong Khoa học Dữ liệu và Phân tích, có hơn 100 công việc được đăng mỗi tháng. Bạn có thể đăng ký tất cả nếu bạn muốn làm điều đó.

Các kênh khác như Upwork , PeoplePerHour , Freelancer , Fiverr , Outvise , Toptal , là các trang / nền tảng làm việc tự do khác cho các hợp đồng biểu diễn liên quan đến khoa học dữ liệu

6] Trở thành tác giả sách kỹ thuật hoặc nhà văn kỹ thuật

Nếu bạn đã là nhà khoa học dữ liệu giỏi, bạn có thể viết một cuốn sách của riêng mình, với sự cộng tác của các nhà khoa học dữ liệu khác. Điều này có thể thực sự khó chịu và quá trình lâu dài. Tôi không nói điều này sẽ giúp bạn thành công trong một sớm một chiều, nhưng điều này sẽ mang lại cho bạn thu nhập thụ động suốt đời.

Bạn có thể xem một số cuốn sách được viết rất đẹp và là một số cuốn sách kinh điển dành cho những người muốn bắt đầu như một người mới bắt đầu trong lĩnh vực khoa học dữ liệu.

Một lần nữa, đây là điều phụ thuộc vào mức độ sâu rộng của mạng lưới của bạn, vì vậy tôi thực sự khuyên bạn nên bắt đầu xây dựng mạng lưới của mình trên các kênh truyền thông xã hội khác nhau và thúc đẩy những người theo dõi của bạn, sau đó thực hiện bước táo bạo này là viết sách của bạn.

7] Phương tiện truyền thông xã hội

Tôi đã thấy nhiều người sử dụng đòn bẩy mạnh mẽ của phương tiện truyền thông xã hội, để đạt được lợi ích tiền tệ. Điều này phù hợp với tất cả những người sáng tạo nội dung và những nhà lãnh đạo tư tưởng trên thế giới.

Bạn có thể tìm thấy một số nhà lãnh đạo tư tưởng thực sự tuyệt vời mà bạn cần tuân theo tại đây:

21 Giáo sư dẫn đầu về Khoa học Dữ liệu (masterindatascience.org)

Bạn có thể bắt đầu đăng hàng ngày về mã hoặc sách mới hoặc điều gì đó thú vị đang xảy ra trong ngành và suy nghĩ của bạn về một số bản phát hành thư viện mới, bất kỳ thứ gì liên quan đến khoa học dữ liệu và xây dựng khán giả của bạn trên bất kỳ nền tảng nào.

Chọn một nền tảng và gắn bó với nó, trong một số tháng, bạn có thể đạt được nhiều khách hàng tiềm năng cho nhiều mục đích khác nhau, nó có thể dành cho giảng viên khoa học dữ liệu ở một số viện hoặc có thể nhận được một số đánh giá về mã hoặc bạn có thể tham gia vào các sự kiện trả phí. Sự phát triển của bạn không chỉ giới hạn ở những nền tảng này, bạn có thể mở rộng sang nhiều kênh khác nhau và phát triển theo cấp số nhân.

Tôi khuyên bạn nên bắt đầu với LinkedIn và kết nối với các nhà khoa học dữ liệu đồng nghiệp. Bạn có thể giới thiệu tốt hồ sơ của mình và là người đi đầu trong lĩnh vực này. Bạn có thể tạo ra một lượng lớn khách hàng tiềm năng từ phương tiện truyền thông xã hội và được trả tiền cho việc tư vấn về những thứ liên quan đến dữ liệu.

Bạn luôn có thể có nhiều cách để kiếm tiền hơn và một trong những cách kiếm tiền hiệu quả nhất là tạo nội dung giáo dục trên các nền tảng như, Udemy, Coursera, v.v. Bạn có thể kết hợp tất cả các cách đã đề cập ở trên hoặc bắt đầu với một hoặc hai và dần dần quyết định về cách bạn muốn tiếp tục.

Liên kết: https://medium.com/faun/7-ways-to-earn-2000-month-as-a-freelance-data-scientist-d74ae4e73fd8

#datascientist

7 способов заработать 2000/месяц в качестве внештатного специалиста по

Сегодня я собираюсь рассказать вам, как именно вы можете зарабатывать на среднем уровне, и, как специалист по науке о данных, я считаю, что поиск работы становится очень трудным, когда вы сталкиваетесь с большим количеством отказов.

Это определенно не кликбейт. Я собираюсь направить вас и помочь вам собрать необходимую информацию о том, как вы можете зарабатывать в качестве внештатного специалиста по данным.

Отказ от ответственности: Информация, представленная здесь, предназначена для людей, которые не сдадутся, и я не несу никакой ответственности, если вы не сможете много заработать. Важный совет для всех моих коллег-читателей: нужно быть терпеливым, последовательным и иметь план. Прежде чем следовать любому из приведенных ниже планов, пожалуйста, изучите его, а затем примите какие-либо меры.

Давайте начнем.

1] Работа консультантом по данным

Есть много свободных рабочих мест, где вы можете получить сотни клиентов, вам просто нужно знать, кого вы можете предложить, и какая компания предложит вам лучшее предложение. Сначала вы можете начать с низких ставок, а затем постепенно, с некоторым опытом, вы можете увеличивать ставки.

Есть много студентов за границей в Великобритании, Канаде, Австралии, США и т. Д., Которым нужна помощь с их проектами или заданиями по науке о данных, вы можете предложить эту помощь, и вы можете заработать на этом.

Как вы узнаете, с чего начать?

Вы можете использовать свой профиль LinkedIn, чтобы общаться со студентами из разных колледжей по всему миру и предлагать свои идеи. Если они согласны, вы можете начать немедленно.

Существует так много рабочих пространств Slack, к которым вы можете присоединиться, и эти рабочие пространства Slack полностью посвящены науке о данных, машинному обучению и всему, что с этим связано.

2] Работайте как

Середина

писатель

Создавайте статьи и продолжайте публиковать их в среде, а также когда вы участвуете в партнерской программе среды.

Возможно, вы не заработаете 1000 долларов за ночь. Вы можете публиковаться в крупных изданиях и зарабатывать хорошие и приличные суммы. Это то, на что можно потратить месяцы, чтобы вырастить и построить медленно, вы также можете публиковать в различных публикациях на Medium.

3] Создайте свою собственную библиотеку/пакет

Вы можете создать свою собственную библиотеку/пакет и опубликовать ее на PyPi. И это можно сделать открытым исходным кодом, или вы можете заработать, продавая его широкой публике.

Эта библиотека/пакет может решать некоторые общие проблемы, с которыми сталкиваются все специалисты по данным при создании своих моделей машинного обучения или около того. Этот продукт может быть предназначен для определенной группы людей или может быть универсальным.

ТензорФлоу

, Kafka , PyTorch — все библиотеки/пакеты. Итак, теперь вы можете понять, насколько важны библиотеки и пакеты. Кроме того, вы также можете определить свою команду и создать что-то вместе. Это в основном делает вашу жизнь проще, и всех остальных.

Для начала не так много библиотек, которые дают социальную автоматизацию. Таким образом, вы можете создавать что-то подобное и продавать их.

4] Создайте и развивайте свой канал YouTube

Это один из лучших вариантов стать идейным лидером, Криш Найк, Sentdex, и многие люди стали известными, а их навыки преподавания превосходны. Для этого вам всего лишь понадобится небольшая сумма вложений на свет и может быть даже микрофон.

Эти инвестиции могут быть плодотворными в долгосрочной перспективе. Вы можете построить свой канал на основе науки о данных, машинного обучения, аналитики, проектов и всего, что может быть темой. Видео «Как сделать» очень распространены, вы можете научить чему-то хорошему библиотечному материалу, который вы создали или, может быть, вы узнали со временем. Вы также можете взять любую книгу по науке о данных и оставить отзывы.

Эти действия можно выполнять либо по одному, либо все за один раз, полностью зависит от того, как вы хотите действовать.

Фото Дэниела Томаса на Unsplash

5] Зарегистрируйтесь в качестве фрилансера на нескольких платформах.

Регистрация на этих платформах дает вам отличный старт, и поначалу может быть сложно получить оплату, но вы должны быть терпеливы. Все работает, и они хорошо работают на этих платформах. Я бы предложил начать с Kolabtree , потому что он не используется.

Он предлагает вам множество вариантов, и вы можете видеть, что только в Data Science and Analysis ежемесячно публикуется более 100 вакансий. Вы можете подать заявку на всех, если вы хотели бы это сделать.

Другие каналы, такие как Upwork , PeoplePerHour , Freelancer , Fiverr , Outvise , Toptal , являются другими фриланс-сайтами/платформами для концертов, связанных с наукой о данных.

6] Станьте автором технических книг или техническим писателем.

Если вы уже являетесь хорошим специалистом по данным, вы можете написать собственную книгу в сотрудничестве с другими специалистами по данным. Это может быть действительно разочаровывающим и долгосрочным процессом. Я не говорю, что это принесет вам успех в одночасье, но это даст вам пассивный доход на всю жизнь.

Вы можете ознакомиться с некоторыми прекрасно написанными книгами, которые являются классическими для тех, кто хочет начать работу в качестве новичка в области науки о данных.

Опять же, это зависит от того, насколько глубока ваша сеть, поэтому я бы предложил начать создавать свою сеть на различных каналах социальных сетей и использовать своих подписчиков, а затем сделать этот смелый шаг и написать свою книгу.

7] Социальные сети

Я видел, как многие люди используют мощные рычаги социальных сетей для получения денежной выгоды. Это касается всех создателей контента и идейных лидеров в мире.

Некоторые действительно удивительные лидеры мнений, за которыми вам нужно следовать, можно найти здесь:

21 ведущий профессор в области науки о данных (mastersindatascience.org)

Вы можете начать ежедневно публиковать информацию о новом коде или книгах, или о чем-то интересном, происходящем в отрасли, и о своих мыслях о некоторых новых выпусках библиотек, обо всем, что связано с наукой о данных, и создать свою аудиторию на любой из платформ.

Выберите одну платформу и придерживайтесь ее в течение нескольких месяцев, вы можете получить много потенциальных клиентов для различных целей, это может быть тренер по науке о данных в некоторых институтах, или это может быть проверка кода, или вы можете участвовать в платных мероприятиях. Ваш рост не ограничивается только этими платформами, вы можете расширяться на различные каналы и расти в геометрической прогрессии.

Я бы посоветовал начать с LinkedIn и связаться с коллегами-исследователями данных. Вы можете хорошо продемонстрировать свой профиль и быть идейным лидером в этой области. Вы можете генерировать большое количество потенциальных клиентов из социальных сетей и получать деньги за консультации по вопросам, связанным с данными.

У вас всегда может быть больше способов заработать, и один из самых эффективных способов заработать — это создавать образовательный контент на таких платформах, как Udemy, Coursera и т. д. Вы можете комбинировать все вышеперечисленные способы или начать с одного или двух и постепенно решать. о том, как вы хотите действовать.

Ссылка: https://medium.com/faun/7-ways-to-earn-2000-month-as-a-freelance-data-scientist-d74ae4e73fd8

#datascientist

作為一名自由數據科學家，每月賺取 2000 美元以上的 7 種方法

今天我要告訴你如何從媒體中賺錢，作為一名核心數據科學人，我相信當你面臨很多拒絕時，找工作會變得非常困難。

這絕對不是點擊誘餌，我將指導您並幫助您收集有關如何作為自由數據科學家獲得收入的必要信息。

免責聲明：此處提供的信息是為不會放棄的人提供的，如果您賺不到多少錢，我不承擔任何責任。對我所有的讀者朋友的重要建議是，你需要耐心、一致並且應該有一個計劃。在遵循以下任何計劃之前，請對其進行研究，然後採取任何行動。

讓我們開始吧。

1] 擔任數據顧問

有許多鬆弛的工作空間，您可以在其中獲得 100 多個客戶，您只需要知道您可以推銷誰，以及哪家公司為您提供最好的交易。最初您可以從低費率開始，然後隨著一些經驗逐漸提高費率。

在英國、加拿大、澳大利亞、美國等地有很多學生在他們的數據科學項目或作業上需要幫助，你可以提供這種幫助，你可以從中獲利。

你怎麼知道從哪裡開始？

您可以使用您的 LinkedIn 個人資料與來自全球各個大學的學生聯繫，並提出您的想法。如果他們同意，您可以立即開始。

您可以加入許多 Slack 工作區，這些 Slack 工作區完全致力於數據科學、機器學習和與之相關的東西。

2] 作為一個工作

中等的

作家

創建文章並繼續在媒體上發布，一旦您加入了媒體合作夥伴計劃。

你可能不會在一夜之間賺到 1000 美元。您可以在主要出版物上發表文章，並且可以獲得可觀的收入。這是你可能需要幾個月的時間才能慢慢成長和建立的東西，你也可以在 Medium 上的各種出版物中發表。

3]創建自己的庫/包

您可以創建自己的庫/包並將其發佈到 PyPi。這可以開源，或者您可以通過將其出售給公眾來獲得它。

這個庫/包可以解決所有數據科學家在創建機器學習模型時面臨的一些普遍問題。該產品可以針對特定的人群，也可以是通用的。

TensorFlow

, Kafka , PyTorch都是庫/包。所以你現在可以理解庫和包的重要性了。此外，您也可以了解您的團隊，並一起構建一些東西。它基本上讓你的生活更輕鬆，其他人也一樣。

首先，提供社交自動化的庫並不多。所以你可以創造類似的東西，然後賣掉它們。

4] 建立和發展您的 YouTube 頻道

這是成長為思想領袖的最佳選擇之一，Krish Naik、Sentdex 和許多人成名並且他們的教學技巧非常出色。要做到這一點，你只需要少量的燈光投資，甚至可能是一個麥克風。

從長遠來看，這些投資可能是富有成效的。您可以基於數據科學、機器學習、分析、項目構建您的頻道，任何東西都可以成為主題。“如何做”視頻非常常見，您可以教授一些您自己創建的或者可能是您隨著時間的推移學到的一些好的圖書館知識。您還可以選擇任何有關數據科學的書籍並提供評論。

這些活動可以一項一項完成，也可以一次完成，完全取決於您要如何進行。

丹尼爾·托馬斯 ( Daniel Thomas ) 在Unsplash上拍攝的照片

5] 在多個平台上註冊為自由職業者

在這些平台上註冊為您提供了一個良好的開端，最初可能很難獲得報酬，但您必須耐心等待。事情確實奏效了，而且它們在這些平台上也運行良好。我建議從Kolabtree開始，因為它尚未開發。

它為您提供了許多選擇，您可以看到，僅在數據科學和分析中，每個月就會發布 100 多個職位。如果您願意，您可以申請全部。

Upwork、PeoplePerHour、Freelancer、Fiverr、Outvise、Toptal等其他渠道是數據科學相關演出的其他自由職業網站/平台

6]成為技術書籍作者或技術作家

如果您已經是優秀的數據科學家，您可以與其他數據科學家合作編寫自己的書。這可能是一個非常令人沮喪的長期過程。我並不是說這會讓你一夜之間取得成功，但這會給你帶來終生的被動收入。

您可以查看一些寫得很漂亮的書籍，並且是一些經典讀物，適合想要開始作為數據科學領域初學者的人。

同樣，這取決於你的網絡有多深，所以我實際上建議開始在各種社交媒體渠道上建立你的網絡並利用你的追隨者，然後邁出大膽的一步來寫你的書。

7]社交媒體

我見過很多人利用社交媒體的強大影響力來獲取金錢利益。這適用於世界上所有的內容創作者和思想領袖。

可以在此處找到您需要遵循的一些非常了不起的思想領袖：

21 位數據科學思想領袖教授 (mastersindatascience.org)

您可以開始每天發布有關新代碼或書籍，或行業中發生的有趣事情以及您對一些新庫版本的想法，任何與數據科學相關的內容，並在任何平台上建立您的受眾。

選擇一個平台並堅持下去，幾個月後，您可以獲得許多潛在客戶，用於多種目的，可能是某些機構的數據科學培訓師，也可能是獲得一些代碼審查，或者您可以參加付費活動。你的增長不僅僅局限於這些平台，你可以拓展到各種渠道，成倍增長。

我建議從 LinkedIn 開始，並與其他數據科學家聯繫。您可以很好地展示您的個人資料，並成為該領域的思想領袖。您可以從社交媒體中獲得大量潛在客戶，並通過諮詢數據相關內容獲得報酬。

你總是可以有更多的賺錢方式，最有效的賺錢方式之一是在 Udemy、Coursera 等平台上創建教育內容。你可以結合上述所有方式，或者從一兩個開始，然後逐漸決定關於你想如何進行。

鏈接：https ://medium.com/faun/7-ways-to-earn-2000-month-as-a-freelance-data-scientist-d74ae4e73fd8

#datascientist

Data Scientist Interview - Machine Learning Project System Design

This is going to be a different videos from my regular Coding tutorials. Today we are talking about High-Level System design round that most FAANG companies do for Data Scientist or Machine Learning Engineers role. This round would be ideally your second or third round.

#datascientist #machinelearning #systemdesign 

WHY You Should Not Become A Data Scientist in 2022

This is 2022 and I don't think Data Scientist is the sexiest job of the century anymore.

In this video I try to explain WHY you should not become a data scientist in 2022 and instead offer 3 different Data / Tech roles that can be better for career your.

A lot of people want to know how to become a data scientist but it's important to know the current state of Data Science market and the advantages and disadvantages of becoming a data scientist.

#datascientist

Webinar to Be A Data Scientist

Data Science is the new field in the era of Industry 4.0. Data Scientist are the analytical data experts who have the technical skills to solve complex problems with various technology stack.
Machine learning, data analytics are the major component with it.

#datascientist 

Introduction to Data Science

Introduction to Data Science

This course will answer all your questions that you have to begin your Journey to become a Data Scientist

Data Science is one of the booming field currently and this course discusses what it takes to become a Data Scientist

This course is for anyone who wants to start their career in Data Science but confused about all the jargon that you are hearing  around the web. This course will answer all your questions that you have to begin your Journey to become a Data Scientist. Kindly note that this course doesn't make you a Data Scientist but this is your first step to understand what is Data Science, Data Analytics, Business Analytics, Machine Learning and their differences and where Industries use them.

What students learn from this course?

- What is Data Science ?

- What is Data Analytics ?

- What is Machine Learning ?

- What is Data Analysis?

- What is Business Analysis ?

Who is this course for ?

- Data Science Enthusiasts

- Python Programmers

- SQL Developers

- Machine Learning Enthusiasts

- People who wants to understand different fields in Data Science

What are the prerequisites ?

- This is a quick intro to the world of Data Science and no prerequisites is needed for this course

What you’ll learn

•    What is Data Science?
•    What is Deep Learning?
•    What is Machine Learning?
•    What is Data Analytics?
•    What is Business Analysis?
•    Where we use Deep Learning, Machine Learning , Data Analytics and Data Science

Are there any course requirements or prerequisites?

•    This is a quick intro to the world of Data Science and no prerequisites is needed for this course

Who this course is for:

•    Python Developers
•    Data Science Enthusiasts
•    Data Analyst
•    Students who want to understand the buzz words of Data Science and its different fields

#datascience #datascientist #machinelearning #python

Data Science with Python - Full Course In 12 Hours

Python And Data Science Full Course | Data Science With Python Full Course In 12 Hours

This video on Python for Data Science will make you understand the basics of data science, important libraries in Python for Data Science such as NumPy, Pandas, and Matplotlib. You will get an idea about the Data Science concepts along with mathematics, statistics, and linear algebra.

• Data Science Basics
• Data Science libraries
• Mathematics for Data Science
• Data Science algorithms using python
• Regularization, PCA, Cost Functions
• Who is a Data Scientist  

#python #datascience #algorithms #datascientist #numpy #pandas #matplotlib #mathematics #statistics #linearalgebr