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Data Science

Kid's Learning Journey

Why this course?

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What is the outcome?

Course content


  1. Introduction to Data Science
      • Python Basic
      • Python List
      • Functions and Packages
      • NumPy
      • Intermediate Python for Data Science
      • Matplotlib
      • Dictionaries & Pandas
      • Logic, Control Flow and Filtering
      • Loops
      • Case Study: E-commerce Statistics
      • Python Data Science Toolbox
      • Functions
      • Default arguments, Variable-length arguments, and scope
      • Lambda functions and Error-handling
      • Iterators Case Study: Twitter
      • Python and Data
      • Importing data from different file types
      • Working with relational databases in Python
      •  Importing data from the Internet
      • Interacting with APIs to import data from the web
      • Diving deep into the Twitter API
      • Tidying data for analysis
      • Combining data for analysis
      • Panda
      • Data ingestion & inspection
      • Exploratory data analysis in Panda
      • Time series in Pandas
      • Extracting and transforming data
      • Advanced indexing
      • Rearranging and reshaping data
      • Grouping data
      • Preparing data
      • Concatenating data
      • Case Study – Summer Olympics, TB dataset, Gapminder
  2. SQL for Data Science
      • Selecting Columns
      • Filtering Rows
      • Aggregate Functions
      • Sorting, Grouping and Joins
      • Basics of Relational Databases
      • Applying Filtering, Ordering and Grouping to Queries
      • Advanced SQL Alchemy Queries
      • Introduction to Joins Outer Joins and Cross Joins
      • Set theory Clauses
      • Subqueries
      • Case Study: IMDB, Census
      •  Data Visualization with Python
      • Customizing Plots
      •  Plotting 2D Arrays
      •  Statistical Plots with Seaborn
      • Analyzing Time series and Images
      • Basic plotting with Bokeh
      •  Layouts, Interactions and Annotations
      • Building interactive apps with Bokeh
      •  Case Study: Gapminder, Apple stock, Olympic
      • Statistical Thinking in Python
      • Graphical exploratory Data Analysis
      • Quantitative exploratory Data Analysis
      • Thinking Probabilistically
      • Discrete variables
      • Continuous variables
      •  Parameter estimation by Optimization
      • Bootstrap confidence intervals
      •  Introduction to Hypothesis testing
      • Case Study: US election results, Lung Cancer
  3. Tableau

The following are the topics to get started with tableau. The intermediate course can be done in 2 ways- focusing on data manipulation and advanced calculations or advanced visualizations. 

      1. Getting your data into Tableau
      2. Preparing the data
      3. Exploring the data
      4. Transforming the data
      5. Order of Operations
      6. Blending vs Joins
      7. Choosing appropriate Visualization Charts
      8. Bringing more insights to data with calculations, filters and parameters
      9. Narrating a story and interacting with the dashboards
* It may vary based on child’s learning pace.

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