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. Learn how Perform a Many of data operations in Python’s popular Seaborn library. Seaborn is a Python data visualization library based on matplotlib.
🐼 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 - Python Seaborn Tutorial | Data Visualization Using Seaborn in Python | Python Seaborn Tutorial
02:59:28 14 - Filling Missing Categorical values in Pandas Dataframes
03:26:37 15 - Case Study 1 - Classify Personal Income, Building Logistic Regression Model, Validating Model accuracy, Removing Insignificant Variables, Improving KNN Model Accuracy.
Accuracy measures in Data Mining
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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IgmGuru’s Data Science with Python certification course has been designed after consulting some of the best in the industry and also the faculty who are teaching at some of the best universities. The reason we have done this is because we wanted to embed the topics and techniques which are practiced and are in demand in the industry – conduct them with the help of pedagogy which is followed across universities – kind of applied data science with python. In doing so, we make our learners more industry/job-ready. IgmGuru’s Data Science with Python online training course is the gateway towards your Data Science career.
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Diving headlong into data sets is a part of the mission for anyone working in data science. Often, this means number-crunching, but what do we do when our data set is primarily text-based? We can use regular expressions. In this tutorial, we’re going to take a closer look at how to use regular expressions (regex) in Python.
Regular expressions (regex) are essentially text patterns that you can use to automate searching through and replacing elements within strings of text. This can make cleaning and working with text-based data sets much easier, saving you the trouble of having to search through mountains of text by hand.
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