About

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.

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

    8. Measure of Spread- Range

    9. Measure of Spread-IQR-Part1

    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

    26. Probabilty -Additional Rule

    27. Probability Additional Rule Example

    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

    14. Adjusted R-squared

    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

About this course

  • 159 lessons
  • 17.5 hours of video content
  • Govt. recognized certificates

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 Spread- Range

  • Measure of Spread-IQR-Part1

  • 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

  • Probabilty -Additional Rule 

  • 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

  • Adjusted R-squared

  • 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

  • Bias & Variance Tradeoff

  • 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
  • 4 Quizzes and 1 final Exam 
  • 1 Real-Life Projects 
  • Access on mobile, Laptop, and TV
  • Lifetime access to videos anytime, anywhere
  • Certificate of completion by Skillarena 
  • Guaranteed Internship 

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