Home | Data Science
Data Science
- Course level: Advanced
- Category: Coding
- Language: English
- Age: 13-17 years
- Course: Python | SQL | Tableau
- Cost: $30/hr.
- Duration: 40hrs
Kid's Learning Journey
- Learn fundamentals of data science and tools required.
- Apply skills to real world data analysis and projection problems via case studies.
- Utilize skills to participate in competitions, real world problems.
- Build fundamentals for applications like Machine Learning.
Why this course?
- This course emphasizes on a practical approach to tools required for data science, and utilizing industry standard tools for data manipulation and visualization.
- Directly exposes students to the set of technologies used universally for a variety of applications from data visualization, summarization, analysis and machine learning.
- Lays a foundation in the form of programming and analytical skills which are applicable to a broad array of domains.
How do we teach?
- All concepts are delivered through practical applications and live, interactive sessions using digital tools available for free and universally.
- The focus is entirely on making sure that the student grasps material well rather than sacrificing quality to match up with the course duration. In essence, the pace of the student and course delivery is identified and matched well at the beginning.
- Students work with concepts through examples, and then directly applying them on a variety of case studies drawn from real world data sets.
What is the outcome?
- Ability to program in Python. Introduction to solvers, data handling, and visualization using libraries like NumPy, Matplotlib, Pandas, Seaborn etc. Data sourcing through APIs and databases. Basics of Statistics and aligned thinking in Python.
- Intermediate to advanced data querying using SQL. Sourcing data in to Python to sanitize, manipulate, analyze, and visualize data.
- Visualization and calculations in Tableau. Data sourcing, preparation, exploration, transformation and manipulations thereafter.
Course content
40 SESSIONS*
- 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
- 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
- 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.
- Getting your data into Tableau
- Preparing the data
- Exploring the data
- Transforming the data
- Order of Operations
- Blending vs Joins
- Choosing appropriate Visualization Charts
- Bringing more insights to data with calculations, filters and parameters
- Narrating a story and interacting with the dashboards
* It may vary based on child’s learning pace.
Course Includes:
- 40 hrs 1:1 Training
- Understanding and applying statistical and programmatic thinking in harmony.
- Introduction to tools like Python, SQL, and Tableau.
Course Requirements:
- You should be excited to learn coding, and ready to take action!
- Prior knowledge of coding is required.
- We will help you learn as we prompt you with practice activities.