Introduction to Data Analytics Course :[BEC_DA_IN] APR 26 – JLY 15

Introduction to Data Analytics Course :[BEC_DA_IN] APR 26 – JLY 15

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About Course

Course Summary

This course introduces the fundamentals of data analytics, from data collection to analysis and visualization. Ideal for beginners, it prepares you for more advanced data science concepts in future courses.

Course Outline:

  • Week 1: Introduction to Data Analytics
  • Week 2: Data Collection and Preparation
  • Week 3: Basic Statistics for Data Analytics
  • Week 4: Data Cleaning Techniques
  • Week 5: Data Visualization Tools
  • Week 6: Introduction to Excel for Data Analytics
  • Week 7: Interpreting and Reporting Data Insights
  • Week 8: Final Project: Analyzing a Data Set

Learning Objectives:

  • Learn the basics of data analytics and data preparation
  • Understand how to visualize and interpret data
  • Use data analysis tools like Excel

Who Should Attend:
Beginners interested in data or professionals looking to use data analytics in their work.

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What Will You Learn?

  • Develop programming skills essential for data science
  • Perform exploratory data analysis to uncover insights
  • Build and evaluate basic machine learning models
  • Create interactive visualisations and reports

Course Content

Week 0: Introduction and installations of applications
Welcome to the beginning of your transformational journey with Odera SBM! Week Zero is all about introductions, orientation, and setting up your digital workspace. Over two days, you’ll meet your facilitator and fellow learners, understand the program structure, and install key software tools like SQL and Power BI that you'll be using throughout the course. This foundational week ensures you're fully prepared for the exciting weeks ahead!

  • Program Introduction and Orientation
  • Software Setup and Installation Workshop
  • Program Introduction and Orientation
  • Software Setup and Installation Workshop

Week 1: Introduction to Programming for Data
Overview of Python/SQL for data analysis Basic programming concepts (variables, loops, functions) Working with libraries (Pandas, Numpy)

Week 2-3: Exploratory Data Analysis (EDA)
Understanding data distribution Identifying patterns and relationships EDA techniques and tools

Week 4: Data Manipulation
Data transformation and cleaning using Python Aggregating and summarising data Dealing with time series data

Week 5-6: Introduction to Machine Learning
Basics of supervised and unsupervised learning Building simple models (linear regression, classification) Evaluating model performance

Week 7: Data Visualisation and Reporting
Advanced visualisation techniques Interactive dashboards (Power BI/Tableau) Automating reports

Week 8: Capstone Project
Applying data analysis techniques to a real-world problem Developing a presentation based on findings and insights

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