Data Science with Python - Beginner to Intermediate

Data Science - Beginner/Intermediate

Data Science is all about extracting insights from massive data to gain competitive edge in the market. It involves the use of different methodologies and statistical principles to discover and read patterns in data.

This training does not assume that candidates have prior knowledge of key concepts in the field of data science. We will dive deeply into each of the 3 pillars of any data science project: Data Extraction, Data Processing & Visualization and Building Predictive Models


  • Have a strong understanding of the python language and how it is applied in data science
  • Master the data science project template
  • Start and complete sample data science projects
  • Build predictive models by selecting, evaluating and fine-tuning machine learning algorithms
what you'll be able to do

Training Schedules & Fees

Professional Training

This is designed for working professionals who want to quickly get equipped for their roles. It is a 5-day intensive, straight-to-the-point training.

  • Date Feb. 4, 2020
  • Duration 2 Months
  • Days and Time Twice a week, 10 am - 12.30 pm
  • Program Type Professional
  • Fee N 250,000

Course Outline

Data Science Overview

  • What is Data Science ?
  • Understanding Data Science Project Cycle
  • Tools for Data Science Project
  • Python For Data Science

Setting Up Working Environment - Anaconda

  • Python Distribution for Data Science
  • Python 3.x Vs Python 2.x
  • Installing the Anaconda Distribution
  • Setting Up Jupyter Notebook

Python - The Language

  • Variables - Your first Python Program
  • Working with Strings
  • Data Structure in Python: List, Tuple, Sets
  • Functions
  • Conditionals: If,If-Else
  • Working with Dictionaries
  • Loops
  • Modules

Getting the Data

  • Data Extraction Introduction
  • Varied Available Data Sources: File, APIs, Databases
  • Working with Data from Files
  • Scraping with Data Python Beautiful Soup
  • Getting Data from Databases

Data Processing and Visualization - Numpy & Pandas

  • Introduction to Numpy and Pandas
  • Investigating Basic Data Structure
  • Selection, Indexing and Filtering
  • Centrality Measure
  • Spread Measure
  • Percentile and Boxplot
  • Variance and Standard Deviation
  • Summary Statistics