data science with python 

Let’s start by stating: Python is the new Excel. In fact, arguably it is much better than Excel, as Python is actively applied by organisations, Facebook, Google, Netflix, PayPal, NASA and to name a few.

Hence, with increasing demand which one of the main reason due to its flexibility and huge library to refer, without a doubt, this will soon be triumph as Data Scientist’s first choice of tools.

Key takeaways

  • Understand how Python works and how to use it for digital applications.
  • Learn to use Numpy Matplotlib, Pandas, Seaborn for Data analysis.
  • Learn to use Sklearn library to solve classification and regression problems.
  • Learn to use Tensorflow to build Deep learning engine.
  • Learn to use Malaya to do local text language analytics.


  • A decent enough laptop with modern browsers like Chrome and Firefox.
  • Google account for Google Colab, https://colab.research.google.com/
  • A good internet infrastructure.
  • Required understanding Mathematics and basic Statistics in Diploma / Foundation / A-Level

day 1: training topics

Refreshing Python programming language

  •  Python language fundamentals, including basic syntax, data types, lists, dictionaries, sets, tuples.

Introduction to linear algebra library, Numpy, and vector programming

  •  What is numpy and why linear algebra is very crucial for data science.

Introduction to dataframe library, Pandas

  •  What is pandas and it offers data structures and operations for manipulating numerical tables and time series.

Pandas Data Structure and important functions

  •  Manipulating 1D and 2D data in Pandas and how to use important functions to do basic data cleaning.

Pandas grouping for aggregation, filtration and transformation

  •  What is aggregation and how to aggregate using Pandas.

Pandas fast indexing and selecting data

  •  We will focus on how to slice, dice, and generally get and set subsets of pandas objects.

Pandas time frame type manipulation

  •  We will focus on how to use Pandas time series and dates functionality for time series dataset.

day 2: training topics

Introduction to Machine learning

  • How to use Multinomial bayes theorem, decision trees, random forests, and various ensemble tree based algorithm to solve simple classification problem.

Introduction to Deep learning

  • Why current industry move towards deep learning as base AI engine and how to use Tensorflow to build simple feed-forward deep learning to solve simple classification problem.

Introduction to Regression problem

  • We will learn data exploration on house pricing dataset, and solve regression problem to predict house prices using various machine learning models.

Introduction to Classification problem

  • We will learn data exploration on telco churn dataset, and solve classification problem to predict which customers will churn for next payment period.

who should attend?

This professional training is designed for Technical Professionals as below:

  • CTO
  • CIO
  • Data Scientist
  • Data Engineer
  • Data Analyst
  • Big Data Department Staff
  • Big Data Project Lead
  • Solution Architect
  • Big Data Executive
  • Software Engineer

From various local and global organizations

Ready to get started?