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Statistical Inference For Data Scientists

Every data scientist must be familiar with the concepts of statistical inference. Therefore, this article aims to provide an overview of statistical inference. It will take you deep into the statistical world of inference in a manner that is easy to grasp and understand.

Some scientists regard statistical inference as one of the most difficult concepts in statistics and understanding it thoroughly can really help them add significant value to their projects and the team they are in.

I will aim to explain statistical inference in a simplified manner so that everyone can understand it.

Probability & Statistics

Article Aim

1. What is the statistical inference?
2. Understanding Statistical Inference Process
3. Test Statistics — Bigger Picture With An Example
4. Hypothesis Testing
5. Types Of Error

1. What Is The Statistical Inference?

Data scientists usually spend a large amount of time to gather and assess data. The data is then used to deduce conclusions using data analysis techniques.

Sometimes these conclusions are observed and the findings are easily described using charts and tables. This is known as descriptive statistics. Other times, we have to explore a measure that is unobserved. This is where the statistical inference comes in.

So far so good. Let’s now understand it

The descriptive statistical inference essentially describes the data to the users but it does not make any inferential from the data. Inferential statistics is the other branch of statistical inference. Inferential statistics help us draw conclusions from the sample data to estimate the parameters of the population. The sample is very unlikely to be an absolute true representation of the population and as a result, we always have a level of uncertainty when drawing conclusions about the population.

As an instance, the data scientists might aim to understand how a variable in their experiment behaves. Gathering all of the data (population) for that variable might be a humongous task. Data scientists, therefore, take a small sample of the population of their target variable to represent the population, and then they perform statistical inference on the small sample(s).

The samples are used to estimate the population

The aim of the data scientists is to generalise from a sample to a population knowing there is a degree of uncertainty. Hence the analyses help them make propositions about the entire population of the data. Sometimes data scientists simulate the samples to understand how the population behaves and for that they make assumptions about the underlying probability distributions of the variable. This is one of the core reasons why the concept of probability is heavily recommended to the data scientists.

Subsequently, a number of hypotheses and claims are made about the properties of the population. Next, the statistical models are used to infer conclusions from the sample to deduce the properties of the population.

The article below provides a thorough understanding of what probability distributions are and I highly recommend everyone to read the article

Understanding Probability And Statistics: The Essentials Of Probability For Data Scientists

Explaining The Key Concepts Of Probability For Statisticians

towardsdatascience.com

2. Understanding Statistical Inference Process

This section will help us understand the process of statistical inference. Let’s assume that the data scientists want to learn about the behaviour of their target variables. They might be interested in understanding how a parameter of a population behaves.

• As an instance, they might want to assess whether all overnight batch jobs across all of the departments in a bank complete within a particular time frame.
• Or, they might want to find the average height of a population in a country.
• Or, maybe they want to understand whether a business made the same profit and the users behaved differently before or after a specific event, such as after a new product was launched.
• Or they want to prove a particular claim about a population wrong.

Occasionally it is too difficult to gather all of the data of a population. Consequently, the data scientists prepare their sample set from the population.

For instance, the parameter the data scientists want to learn about could be the mean or variance of the population. They extract the sample from the population and they can then perform statistical analysis to estimate the population parameter. Sometimes, they check whether the parameter meets a specific value which is believed to be true.

#probability #fintechexplained #data-science #statistics #math #data analysis

Buddha Community

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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

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Data Science: Advice for Aspiring Data Scientists | Experfy Insights

Around once a month, I get emailed by a student of some type asking how to get into Data Science, I’ve answered it enough that I decided to write it out here so I can link people to it. So if you’re one of those students, welcome!

I’ll segment this into basic advice, which can be found quite easily if you just google ‘how to get into data science’ and advice that is less common, but advice that I’ve found very useful over the years. I’ll start with the latter, and move on to basic advice. Obviously take this with a grain of salt as all advice comes with a bit of survivorship bias.

4. Learn Through Research or Entry Level Jobs

#big data & cloud #data science #data scientist #statistics #aspiring data scientist #advice for aspiring data scientists

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50 Data Science Jobs That Opened Just Last Week

Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

In this article, we list down 50 latest job openings in data science that opened just last week.

(The jobs are sorted according to the years of experience r

1| Data Scientist at IBM

**Location: **Bangalore

Skills Required: Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.

Apply here.

2| Associate Data Scientist at PayPal

**Location: **Chennai

Skills Required: Data mining experience in Python, R, H2O and/or SAS, cross-functional, highly complex data science projects, SQL or SQL-like tools, among others.

Apply here.

3| Data Scientist at Citrix

Location: Bangalore

Skills Required: Data modelling, database architecture, database design, database programming such as SQL, Python, etc., forecasting algorithms, cloud platforms, designing and developing ETL and ELT processes, etc.

Apply here.

4| Data Scientist at PayPal

**Location: **Bangalore

Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.

Apply here.

5| Data Science at Accenture

**Location: **Bibinagar, Telangana

Skills Required: Data science frameworks Jupyter notebook, AWS Sagemaker, querying databases and using statistical computer languages: R, Python, SLQ, statistical and data mining techniques, distributed data/computing tools such as Map/Reduce, Flume, Drill, Hadoop, Hive, Spark, Gurobi, MySQL, among others.

#careers #data science #data science career #data science jobs #data science news #data scientist #data scientists #data scientists india

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Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).