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Welcome to this course on Data Science For Beginners With Python Pandas. Learn how Perform a Many of data operations in Python’s popular Pandas library.

🐼 Content 🐼

00:00:01 1 - Introduction to Data Science + Python Environment Setup

00:08:57 2 - Importing Datasets in Pandas and Removing Junk

00:24:23 3 - Copying, Selecting, Indexing data from Pandas Dataframes

00:40:22 4 - Pandas dataframe Counting, Summarized view, Data Types

00:54:26 5 - Cleaning and Converting the Pandas Dataframe columns

01:07:13 6 - If Else and Looping Constructs in Pandas Part 1

01:19:16 7 - If Else and Looping Constructs in Pandas Part 2

01:24:29 8 - Exporting data to file, Aggregate Statistics, Describing

01:35:53 9 - Calculate Frequency Tables, Two way Tables…

01:45:53 10 - Conditional Probability and Correlation in Pandas

01:57:20 11 - Dataframe Visualization using Matplotlib and Seaborn

02:11:23 12 - Numerical and Categorical variables with Matplotlib

02:23:53 13 - Filling Missing Categorical values in Pandas Dataframes

Accuracy measures in Data Mining

Link to notebook and dataset:https://github.com/gshanbhag525/Programming-Knowledge-

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Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

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In this Data Science With Python Training video, you will learn everything about data science and python from basic to advance level. This python data science course video will help you learn various python concepts, AI, and lots of projects, hands-on demo, and lastly top trending data science and python interview questions. This is a must-watch video for everyone who wishes o learn data science and python to make a career in it.

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For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.

“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.

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Learn Data Science is this full tutorial course for absolute beginners. Data science is considered the “sexiest job of the 21st century.” You’ll learn the important elements of data science. You’ll be introduced to the principles, practices, and tools that make data science the powerful medium for critical insight in business and research. You’ll have a solid foundation for future learning and applications in your work. With data science, you can do what you want to do, and do it better. This course covers the foundations of data science, data sourcing, coding, mathematics, and statistics.

⭐️ Course Contents ⭐️

⌨️ Part 1: Data Science: An Introduction: Foundations of Data Science

- Welcome (1.1)
- Demand for Data Science (2.1)
- The Data Science Venn Diagram (2.2)
- The Data Science Pathway (2.3)
- Roles in Data Science (2.4)
- Teams in Data Science (2.5)
- Big Data (3.1)
- Coding (3.2)
- Statistics (3.3)
- Business Intelligence (3.4)
- Do No Harm (4.1)
- Methods Overview (5.1)
- Sourcing Overview (5.2)
- Coding Overview (5.3)
- Math Overview (5.4)
- Statistics Overview (5.5)
- Machine Learning Overview (5.6)
- Interpretability (6.1)
- Actionable Insights (6.2)
- Presentation Graphics (6.3)
- Reproducible Research (6.4)
- Next Steps (7.1)

⌨️ Part 2: Data Sourcing: Foundations of Data Science (1:39:46)

- Welcome (1.1)
- Metrics (2.1)
- Accuracy (2.2)
- Social Context of Measurement (2.3)
- Existing Data (3.1)
- APIs (3.2)
- Scraping (3.3)
- New Data (4.1)
- Interviews (4.2)
- Surveys (4.3)
- Card Sorting (4.4)
- Lab Experiments (4.5)
- A/B Testing (4.6)
- Next Steps (5.1)

⌨️ Part 3: Coding (2:32:42)

- Welcome (1.1)
- Spreadsheets (2.1)
- Tableau Public (2.2)
- SPSS (2.3)
- JASP (2.4)
- Other Software (2.5)
- HTML (3.1)
- XML (3.2)
- JSON (3.3)
- R (4.1)
- Python (4.2)
- SQL (4.3)
- C, C++, & Java (4.4)
- Bash (4.5)
- Regex (5.1)
- Next Steps (6.1)

⌨️ Part 4: Mathematics (4:01:09)

- Welcome (1.1)
- Elementary Algebra (2.1)
- Linear Algebra (2.2)
- Systems of Linear Equations (2.3)
- Calculus (2.4)
- Calculus & Optimization (2.5)
- Big O (3.1)
- Probability (3.2)

⌨️ Part 5: Statistics (4:44:03)

- Welcome (1.1)
- Exploration Overview (2.1)
- Exploratory Graphics (2.2)
- Exploratory Statistics (2.3)
- Descriptive Statistics (2.4)
- Inferential Statistics (3.1)
- Hypothesis Testing (3.2)
- Estimation (3.3)
- Estimators (4.1)
- Measures of Fit (4.2)
- Feature Selection (4.3)
- Problems in Modeling (4.4)
- Model Validation (4.5)
- DIY (4.6)
- Next Step (5.1)

📺 The video in this post was made by freeCodeCamp.org

The origin of the article: https://www.youtube.com/watch?v=ua-CiDNNj30&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=7

🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.

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