The Comprehensive Data Analyst Course

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Newton Academy

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1 - Basic Python for Data Analysis

1 - Keywords Identifiers and Variables.mp4

08:12

2 - Variable Assignment.mp4

06:53

3 - Strings List.mp4

17:13

4 - Tuple.mp4

03:19

5 - Set.mp4

04:19

6 - Dictionary.mp4

05:20

7 - Data type conversion.mp4

09:07

8 - Python Comments.mp4

02:47

9 - Print Statement.mp4

05:40

10 - Python Arithmetic and Logical Operators.mp4

10:02

11 - Identity Membership Operators.mp4

06:05

12 - For While loop.mp4

07:03

13 - Conditional Statement.mp4

02:50

14 - Functions.mp4

19:10

15 - Modules.mp4

07:11

16 - List Part 1.mp4

06:19

17 - List Part 2.mp4

13:26

18 - List Part 3.mp4

10:34

19 - List Part 4.mp4

12:56

20 - List Part 5.mp4

09:27

21 - Tuple Part 1.mp4

06:01

22 - Tuple Part 2.mp4

06:01

23 - Set Part 1.mp4

05:38

24 - Set Part 2.mp4

08:11

25 - Set Part 3.mp4

02:56

26 - Dictionary.mp4

16:38

27 - Strings.mp4

11:16

28 - Numpy Introduction.mp4

08:56

29 - Creating arrays.mp4

16:54

30 - Array Operations Part 1.mp4

12:50

31 - Array Masking.mp4

03:59

32 - Array Operations Part 2.mp4

09:33

33 - Array Operations Part 3.mp4

13:10

34 - Array broadcasting.mp4

06:37

35 - Array Shape Manipulation Sorting.mp4

10:18

2 - Basics of SQL

36 - SQL Introduction.mp4

06:16

37 - Select Command.mp4

05:07

38 - Limit Command.mp4

02:58

39 - Column Filtering.mp4

02:11

40 - DISTINCT command.mp4

09:32

41 - WHERE command.mp4

03:56

42 - AGGREGATE Functions.mp4

06:13

43 - GROUP BY command.mp4

11:53

44 - AND OR NULL commands.mp4

07:58

45 - LIKE command WILDCARD characters.mp4

07:05

46 - JOINS Part 1.mp4

11:18

47 - JOINS Part 2.mp4

09:17

48 - JOINS Part 3.mp4

07:48

49 - IN command.mp4

01:06

50 - HAVING Command.mp4

04:26

51 - UNION command.mp4

01:35

52 - ANY ALL command.mp4

07:15

3 - Principal Component Analysis

53 - Preface for Dimensionality Reduction Part 1.mp4

13:18

54 - Preface for Dimensionality Reduction Part 2.mp4

11:43

55 - Preface for Dimensionality Reduction Part 3.mp4

12:27

56 - Preface for Dimensionality Reduction Part 4.mp4

17:56

57 - Preface for Dimensionality Reduction Part 5.mp4

09:48

58 - Gometric Intuition of PCA.mp4

09:51

59 - Mathematical formulation of PCA Part 1.mp4

16:58

60 - Mathematical formulation of PCA Part 2.mp4

07:22

61 - Mathematical formulation of PCA Part 3.mp4

24:34

62 - Failure cases of PCA.mp4

04:01

63 - Connecting Colab to Gdrive.mp4

06:16

64 - Understanding MNIST dataset.mp4

12:41

65 - Visualizing MNIST single digit.mp4

05:27

66 - MNIST Visualization Method 1.mp4

17:14

67 - MNIST Visualization Method 2.mp4

02:41

Description

Learn about Numpy, Pandas, SQL, Linear Algebra, Visualization and more through solved case study

What You'll Learn?

Basics of Python
Introduction to Numpy package for handling arrays
Introduction to Pandas package for cleaning and analysing data
Introduction to SQL
Basics of Linear Algebra - What is a point, Line, Distance of a point from a line
What is a Vector and Vector Operations
What is a Matrix and Matrix Operations
Visualizing data, including bar graphs, pie charts, histograms
Data distributions, including mean, variance, and standard deviation, and normal distributions and z-scores
Analyzing data, including mean, median, and mode, plus range and IQR and box plots
Data Distributions like Normal and Chi Square
Probability, including union vs. intersection and independent and dependent events and Bayes' theorem
Central Limit Theorem
Hypothesis Testing

Who is this for?

Aspiring Data Analysts

Business Analyst

Business Managers

Anyone wanting to learn basics of story telling through data

More details

Description
THE COMPREHENSIVE DATA ANALYST COURSE IS SET UP TO MAKE LEARNING FUN AND EASY
This 100+ lesson course includes 20+ hours of high-quality video and text explanations of everything from Linear Algebra, Probability, Statistics, Permutation and Combination. Topic is organized into the following sections:

Python Basics, Data Structures - List, Tuple, Set, Dictionary, Strings
Pandas and Numpy
Linear Algebra - Understanding what is a point and equation of a line.
What is a Vector and Vector operations
What is a Matrix and Matrix operations
Data Type - Random variable, discrete, continuous, categorical, numerical, nominal, ordinal, qualitative and quantitative data types
Visualizing data, including bar graphs, pie charts, histograms, and box plots
Analyzing data, including mean, median, and mode, IQR and box-and-whisker plots
Data distributions, including standard deviation, variance, coefficient of variation, Covariance and Normal distributions and z-scores.
Different types of distributions - Uniform, Log Normal, Pareto, Normal, Binomial, Bernoulli
Chi Square distribution and Goodness of Fit
Central Limit Theorem
Hypothesis Testing
Probability, including union vs. intersection and independent and dependent events and Bayes' theorem, Total Law of Probability
Hypothesis testing, including inferential statistics, significance levels, test statistics, and p-values.
Permutation with examples
Combination with examples
Expected Value
Donors Choose case study

AND HERE'S WHAT YOU GET INSIDE OF EVERY SECTION:

We will start with basics and understand the intuition behind each topic.
Video lecture explaining the concept with many real-life examples so that the concept is drilled in.
Walkthrough of worked out examples to see different ways of asking question and solving them.
Logically connected concepts which slowly builds up.
Enroll today! Can't wait to see you guys on the other side and go through this carefully crafted course which will be fun and easy.

YOU'LL ALSO GET:

Lifetime access to the course
Friendly support in the Q&A section
Udemy Certificate of Completion available for download
30-day money back guarantee
Who this course is for:
Aspiring Data Analysts
Business Analyst
Business Managers
Anyone wanting to learn basics of story telling through data

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Data Analysis

Newton Academy

Instructor's Courses

We are a team with >20 years of cumulative experience in IT Consulting and Analytics across multiple domains. We have Program managed large cross functional teams to accomplish global scale projects. We have a reliable track record of working with senior management in Problem Statement Definition, Business Case Creation and providing elegant solutions.We have worked across Big 4 consulting and IT firms.We specialize in conducting Machine Learning & cloud training within our current organization (40+ sessions/ 2000 + trainees) and leading a team to implement ML solutions on Azure.

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