Data Analysis In-Depth (With Python)

Name: Data Analysis In-Depth (With Python)
Rating: 4.6 (65 reviews)

Highest Rated

Created byHarish Masand

Last updated 11/2023

English

What you'll learn

Data Analysis In-depth, Covers Introduction, Statistics, Hypothesis, Python Language, Numpy, Pandas, Matplotlib, Seaborn and Complete EDA
Completing this course will also make you ready for most interview questions for Data Analysts Role
This is Pre-requisite for Machine Learning, Deep Learning, Reinforcement Learning, NLP, and other AI courses
Includes Optional Project and path to success

Course content

1 section • 24 lectures • 52h 41m total length

Introduction to Data Science Career Path1:33
Day 1 - Introduction to Data Science2:09:39
Day 2 - Introduction to Data Analytics1:58:44
Day 3 - Statistics for Data Analysis - Scalar, Vectors and Matrix3:26:19
Day 4 - Statistics for Data Analysis - Probability2:47:41
Explore probability fundamentals for data analysis, outlining sample space, events, union, intersection, and complement, and compare independent, dependent, and replacement scenarios with practical examples.
Day 5 - Statistics for Data Analysis - Probability2:37:39
Day 6 - Statistics for Data Analysis - Probability2:46:22
Day 7 - Statistics for Data Analysis - Probability2:20:29
Day 8 - Statistics for Data Analysis - Statistical Hypothesis3:01:40
Explore statistical hypothesis testing by framing null and alternate hypotheses, distinguishing population and sample, and using alpha, p-values, and type i and ii errors to validate data claims.
Day 9A - Statistics for Data Analysis - Statistical Hypothesis37:08
Explore statistical hypothesis testing with degree of freedom, population standard deviation, sample size, and p-values, including two-sample t-tests, alpha levels, and practical a/b style comparisons.
Day 9B - Python for Data Analysis2:04:17
Explore why Python dominates data analysis and machine learning, including its libraries, portability, and how to use tools like Google Colab and Jupyter Notebook for scalable analytics.
Day 10 - Python for Data Analysis2:21:14
Explores Python basics for data analysis, including data types, strings, lists, and dictionaries, indexing, slicing, mutability, and common Python built-ins, with practical notebook workflows in Jupyter and Colab.
Day 11 - Python for Data Analysis2:44:55
Practice Python data analysis fundamentals by working with lists, dictionaries, and comprehensions; learn sorting, indexing, and function fundamentals including scope and imports.
Day 12 - Python for Data Analysis2:24:11
Day 13 - Numpy2:56:09
Day 14 - Pandas2:36:00
Day 15 - Pandas1:53:41
Explore core pandas workflows for data analysis: create and inspect dataframes, index and select data, apply functions, compute summaries, sort, handle missing values, and create dummies.
Day 16 - Pandas2:47:18
Explore pandas dataframes, one-hot encoding with get_dummies, indexing, and data preparation techniques for machine learning, including handling missing data, column operations, and reading csv or excel files.
Day 17 - Pandas2:44:29
Day 18 - Seaborn2:46:15
Day 18 covers Seaborn, a Python plotting library built on Matplotlib that simplifies data visualization with distributions, correlations, and plots like box, violin, and heatmaps.
Day 19 A - Seaborn1:28:22
Day 19 B - EDA50:01
Explore the foundations of exploratory data analysis using a LendingClub loan dataset to understand data distribution, handle missing values and outliers, and engineer informative features for modeling.
Day 20 A - EDA Cont2:52:11
Perform exploratory data analysis on the LendingClub loan dataset in Python, clean data by dropping columns with over 30 percent missing values, and prepare categorical features for seaborn visualizations.
Day 20 B - What Next, How to prepare for Job/Career Switch24:49

Requirements

No Pre-requisite required. Curiosity to learn.

Description

Data Analysis In-Depth (With Python)

1. What will students learn in your course?

Data Analysis In-depth, Covers Introduction, Statistics, Hypothesis, Python Language, Numpy, Pandas, Matplotlib, Seaborn and Complete EDA

Completing this course will also make you ready for most interview questions for Data Analysts Role

This is Pre-requisite for Machine Learning, Deep Learning, Reinforcement Learning, NLP, and other AI courses

Includes Optional Project and path to success

2. What are the requirements or prerequisites for taking your course?

No Pre-requisite required. Curiosity to learn.

3. Who is this course for?

People looking to advance their career in Data Science and Data Analytics

Already working in Data Science/ Data Analyst Roles and want to clear the concepts

Want to make base strong before moving to Machine Learning, Deep Learning, Reinforcement Learning, NLP, and other AI courses

Currently working as Business Analyst / Analyzing data in Excel, Tableau, Qlik, Power BI, etc. And want to do scalable and automated analysis in Python.

4. Is this course in depth and will make industry ready?

Absolutely yes, it will make you ready to creach Data Analyst roles interview as well as it is pre requisite for Machine Learning, Deep Learning, etc

5. I am new to IT/Data Science, Will i understand?

Absolutely yes, it is taught in most simplest way for every one to understand

Who this course is for:

People looking to advance their career in Data Science and Data Analytics
Already working in Data Science/ Data Analyst Roles and want to clear the concepts
Want to make base strong before moving to Machine Learning, Deep Learning, Reinforcement Learning, NLP, and other AI courses
Currently working as Business Analyst / Analyzing data in Excel, Tableau, Qlik, Power BI, etc. And want to do scalable and automated analysis in Python.