/Data-Scientist-with-Python

This repo comprises of my solutions for the tasks assigned in the course.

Data-Scientist-with-Python

Data Camp Track

01 - Introduction to Python

  1. Python Basics
  2. Python Lists
  3. Functions and Packages
  4. NumPy

01 - Course

01 - Assignments

02 - Intermediate Python

  1. Matplotlib
  2. Dictionaries & Pandas
  3. Logic, Control Flow and Filtering
  4. Loops
  5. Case Study: Hacker Statistics

02 - Course

02 - Assignments

03 - Investigating Netflix Movies and Guest Stars in The Office

03 - About the project

Project notebook

04 - Data Manipulation with pandas

  1. Transforming DataFrames
  2. Aggregating DataFrames
  3. Slicing and Indexing DataFrames
  4. Creating and Visualizing DataFrames

04 - Course

04 - Assignments

C4

05 - Joining Data with pandas

  1. Data Merging Basics
  2. Merging Tables With Different Join Types
  3. Advanced Merging and Concatenating
  4. Merging Ordered and Time-Series Data

05 - Course

05 - Assignments

06 - Introduction to Statistics in Python

  1. Summary Statistics
  2. Random Numbers and Probability
  3. More Distributions and the Central Limit Theorem
  4. Correlation and Experimental Design

06 - Course

06 - Assignments

C6

07 - The GitHub History of the Scala Language

07 - About the project

Project notebook

08 - Introduction to Data Visualization with Matplotlib

  1. Introduction to Matplotlib
  2. Plotting time-series
  3. Quantitative comparisons and statistical visualizations
  4. Sharing visualizations with others

08 - Course

08 - Assignments

09 - Introduction to Data Visualization with Seaborn

  1. Introduction to Seaborn
  2. Visualizing Two Quantitative Variables
  3. Visualizing a Categorical and a Quantitative Variable
  4. Customizing Seaborn Plots

09 - Course

09 - Assignments

10 - Introduction to NumPy

  1. Understanding NumPy Arrays
  2. Selecting and Updating Data
  3. Array Mathematics!
  4. Array Transformations

10 - Course

10 - Slides

10 - Assignments

11 - Python Data Science Toolbox (Part 1)

  1. Writing your own functions
  2. Default arguments, variable-length arguments and scope
  3. Lambda functions and error-handling

11 - Course

11 - Assignments

12 - The Android App Market on Google Play

12 - About the project

Project notebook

13 - Python Data Science Toolbox (Part 2)

  1. Using iterators in PythonLand
  2. List comprehensions and generators
  3. Bringing it all together!

13 - Course

13 - Assignments

14 - Intermediate Data Visualization with Seaborn

  1. Seaborn Introduction
  2. Customizing Seaborn Plots
  3. Additional Plot Types
  4. Creating Plots on Data Aware Grids

14 - Assignments

C14

15 - Data Communication Concepts

  1. Storytelling with Data
  2. Preparing to communicate the data
  3. Structuring written reports
  4. Building compelling oral presentations

15 - Course

18 - Introduction to Importing Data in Python

  1. Introduction and flat files
  2. Introduction to Importing Data in Python
  3. Working with relational databases in Python

18 - Course 18 - Assignments