The course provides the entire toolbox you need to become a Data Scientist.

Fill up your resume with in-demand data science skills like Statistical analysis, Python programming, NumPy, Pandas, Advanced Statistical Analysis, Power BI, Machine Learning with Stats models.

## Learn from the Best ### Data Science Megha Kansal

A Data Scientist Ms. Megha Kansal is having 9 years of experience in Data Science, SIT Testing, Machine Learning, and R, in addition to leading the data science division at Genpact, Ms. Megha is on a mission to spread knowledge about Data Science to students. With her knowledge and expertise, start your journey of becoming a certified Data Scientist with Skillarena.

## What You'll Learn

Python:

• Python Installation

• Introduction to Python

• Python Libraries And NB Intro

• Jupyter Notebook

• Python Basics

• Data Type String

• Data Type Booloean And Built-In Functions

• Lists

• List Exercise

• Dictionary

• Tuples

• Sets

Numpy:

• Numpy number Generating Techniques

• Numpy Indexing

• Functions and Arithmatic Operations

Pandas:

• Pandas Basics

• Pandas Analyse Data

• Pandas Data Cleaning

• Pandas Null Duplicate Values

• Pandas Indexing

• Pandas Merge

Data Visualization:

• Matplotlib

• Maplot figutre function

• Matplotlib bar

• Matplotlib Histo

Statistics:

• Statistics Basics

• Statistics Basic Data Collection

• Desriptive Statistics

• Arithmatic Mean

• Median, Mode

• Measure of Dispersion-IQR part 2

• Measure of Spread-IQR part 3

• Standard Deviation & Variance

• Standard Deviation & Variance Example

• Empirical Rule

• Z Score

• Shape-Skweness

• Covariance

• Coefficient of Correlation

• Statistics in Python

• What is probability

• Types of Probability

• Mutually Exclusive Independent Event

• Union Intersection

• Exhaustive and Compliment

• Probability Multiplication Rule

• Bayes Theorem

• Central Limit System

• Hypothesis Testing

• Hypothesis Type I & II Error

• Hypothesis Observed Value

• Hypothesis Level of Significance

• Hypothesis LOS Critical Value

• Hypothesis Testing Case Study

• p – value

• Degrees of Freedom

• t Test

• Chi Square test

• Chi-square test example in excel

• Chi-square using  python

Machine Learning:

• Machine Learning Intro

• What is Machine Learning & Examples

• What is Supervised Learning and Examples

• What is Unsupervised Learning and Examples

• Linear Regression

• Linear Regression Rsquare

• Uses & Assumptions of Linear Regression

• Multiple Linear Regression

• Linear Regression-Python Model Creation & Prediction

• Model Evaluation

• Difference in Actual & Prediction

• Collinearity

• What is Logistic Regression

• Logistics Regression Example

• Logistic Regression Equation

• Logistic Regression-Logit

• Confusion Matrix

• Logistic Regression ROC

• Logistic Regression AUC

• Logistic Regression Practical Python

• Logistic Regression -Python practicle

• Logistic Regression-Python Practice-Predictions

• Overfitting & Underfitting

• What is K Nearest Neighbor (KNN)

• KNN Explanation

• KNN - How to compute

• KNN Python-Data Preprocession

• KNN Python-Model Creation

• KNN Python-Prediction

• Decision Tree

• Decision Tree-Python Case Study

• Ensemble Technique – Bagging

• Boosting

• Random Forest

• Random Forest-Python Case Study

• What is Clustering

• Clustering Application & Flow

• Cluster-Distance

• Hierarchical Clustering

• Clustering Steps

• K-Means Clustering

• K Means Clustering Algorithm Python

Power BI:

• Power BI Intro

• Power BI Installation & About Power BI

• Power BI-Tour

• Get Data

• Edit Query

• Power BI Transform Data

• Data Modelling

• Visualization

## What this Course Includes

• 151 videos

• 17 hours of on-demand video

• 45 Days of Learning Program

• 4 Quizzes and 1 final Exam

• 1 Real-Life Projects

• Access on mobile, Laptop, and TV

• Certificate of completion by Skillarena

• Live Sessions

• Internship Opportunities

## Requirements

System Pre-requisites:-

8GB RAM Recommended

Windows 10 is recommended.

Browsers - Edge, Chrome.

At least 500 GB of Hard Disk would be the minimum requirement as data sets you practice tend to be heavy.

Willingness to Learn and Explore

## Course curriculum

1. Promo Video

FREE PREVIEW
2. Python Introduction P1

FREE PREVIEW
3. Python Installation P2

4. Python Installation P3

5. Python Libraries And NB Intro

6. Python Libraries And NB Intro part -2

7. Jupyter Notebook

8. Python Basics

9. Python Basics part - 2

10. Python Basics part - 3

11. Data Type String

12. Data Type Boolean And Built-In Functions

13. Lists part - 1

14. Lists part - 2

15. Lists part - 3

16. Lists part - 4

17. List Exercise

18. Dictionary

19. Dictionary part - 2

20. Tuples P1

21. Tuples P2

22. Sets P1

23. Sets P2

24. Sets P3

25. Course Instruction

1. Numpy

2. Numpy Number Generating Techniques

3. Numpy Indexing

4. 4- Numpy Indexing Part 2

1. Pandas Basics

2. Pandas Analyze Data

3. Pandas Data Cleaning Part 1

4. Pandas Null Duplicate Values (1)

5. 4 - Pandas Null Duplicate Values (2)

6. Pandas Indexing

7. Pandas Merge

8. Quiz - 1

1. Matplotlib (1)

2. Matplotlib (2)

3. Matplot figure function

4. 3 - Matplotlib bar

5. Matplotlib Histo

1. Statistics Basics Part 1

2. Statistics Basics Part 2

3. Statistics Basic 2 Data Collection Part 1

4. Statistics Basic 2 Data Collection Part 2

5. Desriptive Statistics

6. 4-Arithmatic Mean

7. Median, Mode

10. Measure of Dispersion-IQR part 2

11. Measure of Spread-IQR part 3

12. Standard Deviation & Variance

13. Standard Deviation & Variance Example

14. Empirical Rule

15. Z Score (1)

16. Shape-Skweness

17. Covariance

18. Covariance Example

19. Coefficient of Correlation

20. Statistics in Python

21. What is probability

22. Types of Probability

23. Mutually Exclusive Independent Event

24. Union Intersection

25. Exhaustive and Compliment

28. Probability Multiplication Rule

29. Bayes Theorem

30. Bayes Theorem Case Study

31. Central Limit System

32. Hypothesis Testing

33. Hypothesis Type I & II Error

34. Hypothesis Observed Value

35. Hypothesis Level of Significance

36. Hypothesis LOS Critical Value

37. Hypothesis Testing Case Study

38. P - value

39. Degrees of Freedom

40. T Test

41. Chi Square test

42. Chi-square test example in excel-part 1

43. Chi square test example in excel-part 2

44. QUIZ - 2

45. Course Instruction

1. Machine Learning Intro

2. What is Machine Learning & Examples

3. What is Supervised Learning

4. 4-Supervised Learning Example

5. What is Unsupervised Learning

6. Unsupervised Learning Example

7. Linear Regression part1

8. Linear Regression Rsquare

9. Uses & Assumptions of Linear Regression

10. Multiple Linear Regression

11. Linear Regression-Python Model Creation & Prediction

12. Model Evaluation

13. Difference in Actual & Prediction

15. Collinearity

16. What is Logistic Regression

17. Logistics Regression Example

18. 18-Logistic Regression Equation

19. Logistic Regression-Logit

20. Confusion Matrix

21. Logistic Regression ROC

22. Logistic Regression AUC

23. Logistic Regression Practical Python-part 1

24. Logistic Regression -Python practical - Part 2

25. Logistic Regression-Summary- part 3

26. Logistic Regression-Python Practice-Predictions-part4

27. Bias & Variance Trade off

28. Overfitting & Underfitting

29. What is K Nearest Neighbor (KNN)

30. KNN Explanation

31. KNN - How to compute

32. KNN Python-Data Preprocession - part 1

33. KNN Python-Model Creation-part2 (1)

34. KNN Python-Model Creation-part2

35. KNN Python-Prediction-part3

36. Decision Tree

37. Decision Tree Explanation

38. Decision Tree-Python Case Study

39. Decision Tree - Python Case Study - part 2

40. Ensemble Technique - Bagging

41. Boosting

42. Random Forest

43. Random Forest-Python Case Study

44. What is Clustering

45. Clustering Application & Flow

46. Cluster-Distance

47. Hierarchical Clustering

48. Clustering Steps

49. K-Means Clustering

50. K Means Clustering Algorithm Python

51. Machine Learning Complete Recape

52. Quiz - 3 