Python for Data Science Fundamentals
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Kick-Start Data Science with Python – From Raw Data To A Simple Model

Starting with loading data, we will move towards learning each step of the data science process using Python.

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“Python is one of the most popular language to learn data science...

Course Synopsis

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Data science is an interdisciplinary field of scientific methods, processes, algorithms and systems. It makes use techniques from various fields as well as programming tools such as Python in order to analyse, uncover patterns and understand data. Using the insights gained, a model can then be created.
This training covers the fundamentals of Python and related libraries as well the data science process such as data wrangling, exploratory data analysis and simple modelling.

Course Objectives

This is a hands-on course that provides the participant with a firm foundation on using Python and its libraries such as Numpy, Pandas and Matplotlib. It also guides the participants with a step-by-step description of the data science process.

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Who Must Attend

Lecturers, students, programmers, developers, engineers and simply anyone who would like to have a firm foundation on Data Science fundamentals before moving on to advanced techniques such as Machine Learning and Deep Learning.

 

Prerequisites

Candidates must have experience with any programming language, and preferably with some statistical knowledge.

 

 

 


Course Outline

Day 1

Introduction to Data Science

  • What is Data Science

  • Why Python

Getting Started with Python

  • Python Installation

  • Installing the libraries

  • Basic Operations

Numpy Fundamentals

  • First look at Numpy arrays

  • Basic operations on the arrays

  • Manipulating the arrays

Pandas Fundamentals

  • First look at Pandas data structure (Series, Dataframes)

  • Reading and Writing data from different file formats

  • Analysing data with Pandas

Matplotlib Fundamentals

  • Visualization using Matplotlib

  • Creating simple plots

 

Day 2

The Data Science Process

  • Introduction

Data Wrangling

  • Retrieving and loading data

  • Cleaning data

  • Converting raw data into usable data

Exploratory Data Analysis

  • Understanding data using summary statistics

  • Finding patterns using visualization

Modelling the Data

  • Creating models using linear regression

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