Our Data Analysis course is perfect for both freshers and working professionals. This 100% hands-on course teaches you Inferential Statistics, Regression, Supervised Machine Learning and more.

1. Brief intro to Python

2. Environment Set-up: Setting up Anaconda in Windows Machine

3. Python Numpy:Intro

4. Numpy Arrays

5. Numpy Arrays Indexing

6. Numpy Operations

7. Python Pandas:Intro

8. Pandas Series

9. Pandas dataframe

10. Missing Data

11. Groupby Method

12. Merging,joining and Concatenation

13. Pandas Operation

14. Data Input and Output

15. Python Matplotlib:Intro

16. Matplotlib visualization overview_part_1

17. Matplotlib visualization overview_part_2

18. Matplotlib visualization overview_part_3

1. Introduction to statistics

2. Popoulation and Sample

1. Buliding Tables and Graphs using excel

2. Central tendancy - Arithmatic mean

3. Central tendancy - Weighted mean

4. Central tendancy - Geometric mean

5. Basic understanding of gaussian curve

6. Measure of dispersion - Range example

7. Measure of dispersion - Variance,standard deviation,COV [With Examples]

8. Measure of dispersion - Inter quartile range

9. Measure of dispersion - Kurtosis

1. Sampling Distribution

2. Central Limit Theorem

3. Confidence Interval

4. Confidence Interval using z test

5. Confidence Interval using t test

6. Confidence Interval simulation

7. What is Hypothesis testing

8. Hypothesis example 1 - part 1 and 2

9. Hypothesis example 2

1. Type I and Type II errors

2. Types of data

3. Types of Variables

4. Variance Inflation Factor

5. R square and adjusted r square

6. Idea behind regression

7. Interpreting regression output

8. Logistic Regression using excel

1. EDA using pandas,Numpy,Matplotlib,Seaborn

1. Intro to Machine Learning

2. Linear Regression theory

3. Linear Regression with Python

4. Hands on example using python sci - Kit learn module 1

5. Logistic Regression Theory

6. Logistic Regression Hands on withPython

7. Hands on example using Pytbon sci - kit learn module 2

8. K Nearest Neighbors Theory

9. K Nearest Neighbors Hands on with Python 1

10. K Nearest Neighbours Hands on wuth Python 2

11. Naive Bayes Theory

12. Decision Tree Classifier Theor

13. Decision Tree Hands on example using python

14. Random Forest Theory

15.Random Forest hands on with python

16. Support Vector Machines (SVM) Theory

17. Support Vector Machine hands on with python 1

18. Support Vector Machine hands on with python 2

19. Intro to Neural Networks

20. Intro to Neural Networks hands on with python

1. Intro to unsupervised learning

2. K Means clustering

3. K Means Example

4. Hierarchical Clustering

5. Hierarchical Clustering - Example

6. Idea Behind PCA

7. PCA Working

8. PCA Example

1. Intro to Text Analytics

2. Text Normalization - Removing punctuation

3. Text Normalization - Normalizing case and Removing stopwards - 2

4. Text Normalization - Stemming,Lemmatization and POS tagging

5. Feature Extraction - Bag of Words - 1

6. Text classification hands on using Naive Bayes Algorithm

1. Introduction to SQL

2. Downloading and Installing SQL server and SSMS

3. Database,datatypes,variables and Tables

4. Primary Key and Foreign key

5. Basic Queries in SQL

6. Aggregate Functions in SQL

7. Group by and Joins in SQL

8. SQL and python