Use Python for Analyzing Visualizing and Presenting Data: Use Python for Analyzing, Visualizing and Presenting Data

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Get ready for your exam by enrolling in our comprehensive training course. This course includes a full set of instructional videos designed to equip you with in-depth knowledge essential for passing the certification exam with flying colors.

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Video Courses

Setup

Lectures	Duration
1. Installation Setup and Overview	7m
2. IDEs and Course Resources	11m
3. iPython/Jupyter Notebook Overview	15m

1. Installation Setup and Overview

2. IDEs and Course Resources

11m

3. iPython/Jupyter Notebook Overview

15m

Learning Numpy

Lectures	Duration
1. Creating arrays	7m
2. Using arrays and scalars	5m
3. Indexing Arrays	14m
4. Array Transposition	4m
5. Universal Array Function	6m
6. Array Processing	22m
7. Array Input and Output	8m

1. Creating arrays

2. Using arrays and scalars

3. Indexing Arrays

14m

4. Array Transposition

5. Universal Array Function

6. Array Processing

22m

7. Array Input and Output

Intro to Pandas

Lectures	Duration
1. Series	14m
2. DataFrames	18m
3. Index objects	5m
4. Reindex	16m
5. Drop Entry	6m
6. Selecting Entries	10m
7. Data Alignment	10m
8. Rank and Sort	6m
9. Summary Statistics	23m
10. Missing Data	12m
11. Index Hierarchy	14m

1. Series

14m

2. DataFrames

18m

3. Index objects

4. Reindex

16m

5. Drop Entry

6. Selecting Entries

10m

7. Data Alignment

10m

8. Rank and Sort

9. Summary Statistics

23m

10. Missing Data

12m

11. Index Hierarchy

14m

Working with Data: Part 1

Lectures	Duration
1. Reading and Writing Text Files	10m
2. JSON with Python	4m
3. HTML with Python	5m
4. Microsoft Excel files with Python	4m

1. Reading and Writing Text Files

10m

2. JSON with Python

3. HTML with Python

4. Microsoft Excel files with Python

Working with Data: Part 2

Lectures	Duration
1. Merge	21m
2. Merge on Index	13m
3. Concatenate	9m
4. Combining DataFrames	10m
5. Reshaping	8m
6. Pivoting	6m
7. Duplicates in DataFrames	6m
8. Mapping	4m
9. Replace	3m
10. Rename Index	6m
11. Binning	6m
12. Outliers	7m
13. Permutation	5m

1. Merge

21m

2. Merge on Index

13m

3. Concatenate

4. Combining DataFrames

10m

5. Reshaping

6. Pivoting

7. Duplicates in DataFrames

8. Mapping

9. Replace

10. Rename Index

11. Binning

12. Outliers

13. Permutation

Working with Data: Part 3

Lectures	Duration
1. GroupBy on DataFrames	18m
2. GroupBy on Dict and Series	13m
3. Aggregation	13m
4. Splitting Applying and Combining	10m
5. Cross Tabulation	5m

1. GroupBy on DataFrames

18m

2. GroupBy on Dict and Series

13m

3. Aggregation

13m

4. Splitting Applying and Combining

10m

5. Cross Tabulation

Data Visualization

Lectures	Duration
1. Installing Seaborn	2m
2. Histograms	9m
3. Kernel Density Estimate Plots	26m
4. Combining Plot Styles	6m
5. Box and Violin Plots	9m
6. Regression Plots	19m
7. Heatmaps and Clustered Matrices	17m

1. Installing Seaborn

2. Histograms

3. Kernel Density Estimate Plots

26m

4. Combining Plot Styles

5. Box and Violin Plots

6. Regression Plots

19m

7. Heatmaps and Clustered Matrices

17m

Example Projects.

Lectures	Duration
1. Data Projects Preview	3m
2. Intro to Data Projects	5m
3. Titanic Project - Part 1	17m
4. Titanic Project - Part 2	16m
5. Titanic Project - Part 3	16m
6. Titanic Project - Part 4	2m
7. Intro to Data Project - Stock Market Analysis	3m
8. Data Project - Stock Market Analysis Part 1	11m
9. Data Project - Stock Market Analysis Part 2	18m
10. Data Project - Stock Market Analysis Part 3	10m
11. Data Project - Stock Market Analysis Part 4	7m
12. Data Project - Stock Market Analysis Part 5	28m
13. Data Project - Intro to Election Analysis	2m
14. Data Project - Election Analysis Part 1	18m
15. Data Project - Election Analysis Part 2	21m
16. Data Project - Election Analysis Part 3	15m
17. Data Project - Election Analysis Part 4	26m

1. Data Projects Preview

2. Intro to Data Projects

3. Titanic Project - Part 1

17m

4. Titanic Project - Part 2

16m

5. Titanic Project - Part 3

16m

6. Titanic Project - Part 4

7. Intro to Data Project - Stock Market Analysis

8. Data Project - Stock Market Analysis Part 1

11m

9. Data Project - Stock Market Analysis Part 2

18m

10. Data Project - Stock Market Analysis Part 3

10m

11. Data Project - Stock Market Analysis Part 4

12. Data Project - Stock Market Analysis Part 5

28m

13. Data Project - Intro to Election Analysis

14. Data Project - Election Analysis Part 1

18m

15. Data Project - Election Analysis Part 2

21m

16. Data Project - Election Analysis Part 3

15m

17. Data Project - Election Analysis Part 4

26m

Machine Learning

Lectures	Duration
1. Introduction to Machine Learning with SciKit Learn	13m
2. Linear Regression Part 1	18m
3. Linear Regression Part 2	18m
4. Linear Regression Part 3	19m
5. Linear Regression Part 4	22m
6. Logistic Regression Part 1	14m
7. Logistic Regression Part 2	14m
8. Logistic Regression Part 3	12m
9. Logistic Regression Part 4	22m
10. Multi Class Classification Part 1 - Logistic Regression	19m
11. Multi Class Classification Part 2 - k Nearest Neighbor	23m
12. Support Vector Machines Part 1	13m
13. Support Vector Machines - Part 2	29m
14. Naive Bayes Part 1	10m
15. Naive Bayes Part 2	12m
16. Decision Trees and Random Forests	32m
17. Natural Language Processing Part 1	7m
18. Natural Language Processing Part 2	16m
19. Natural Language Processing Part 3	21m
20. Natural Language Processing Part 4	16m

1. Introduction to Machine Learning with SciKit Learn

13m

2. Linear Regression Part 1

18m

3. Linear Regression Part 2

18m

4. Linear Regression Part 3

19m

5. Linear Regression Part 4

22m

6. Logistic Regression Part 1

14m

7. Logistic Regression Part 2

14m

8. Logistic Regression Part 3

12m

9. Logistic Regression Part 4

22m

10. Multi Class Classification Part 1 - Logistic Regression

19m

11. Multi Class Classification Part 2 - k Nearest Neighbor

23m

12. Support Vector Machines Part 1

13m

13. Support Vector Machines - Part 2

29m

14. Naive Bayes Part 1

10m

15. Naive Bayes Part 2

12m

16. Decision Trees and Random Forests

32m

17. Natural Language Processing Part 1

18. Natural Language Processing Part 2

16m

19. Natural Language Processing Part 3

21m

20. Natural Language Processing Part 4

16m

Appendix: Statistics Overview

Lectures	Duration
1. Intro to Appendix B	3m
2. Discrete Uniform Distribution	6m
3. Continuous Uniform Distribution	7m
4. Binomial Distribution	13m
5. Poisson Distribution	11m
6. Normal Distribution	6m
7. Sampling Techniques	5m
8. T-Distribution	5m
9. Hypothesis Testing and Confidence Intervals	20m
10. Chi Square Test and Distribution	3m
11. Bayes Theorem	10m

1. Intro to Appendix B

2. Discrete Uniform Distribution

3. Continuous Uniform Distribution

4. Binomial Distribution

13m

5. Poisson Distribution

11m

6. Normal Distribution

7. Sampling Techniques

8. T-Distribution

9. Hypothesis Testing and Confidence Intervals

20m

10. Chi Square Test and Distribution

11. Bayes Theorem

10m

Appendix: SQL and Python

Lectures	Duration
1. Introduction to SQL with Python	10m
2. SQL - SELECT,DISTINCT,WHERE,AND & OR	10m
3. SQL WILDCARDS, ORDER BY, GROUP BY and Aggregate Functions	8m

1. Introduction to SQL with Python

10m

2. SQL - SELECT,DISTINCT,WHERE,AND & OR

10m

3. SQL WILDCARDS, ORDER BY, GROUP BY and Aggregate Functions

Appendix: Web Scraping with Python

Lectures	Duration
1. Web Scraping Part 1	12m
2. Web Scraping Part 2	12m

1. Web Scraping Part 1

12m

2. Web Scraping Part 2

12m

Appendix: Python Special Offers

Lectures	Duration
1. Python Overview Part 1	19m
2. Python Overview Part 2	12m
3. Python Overview Part 3	10m

1. Python Overview Part 1

19m

2. Python Overview Part 2

12m

3. Python Overview Part 3

10m