Introduction to Data Science

Introduction to Data Science

The world has entered the Dig Data phase now, and need of Data Science is growing exponentially with introduction to Data Science in businesses, industries, decision making. There are tools that are used for data science and Data Analytics such as “Hadoop” “R” and others.

Data Science is the future of Artificial Intelligence. Therefore, it is very important to understand what is Data Science and how can it add value to your business.

In this blog, I will be covering the following topics:

  • What is Data Science and need for Data Science
  • What is Data Science?
  • How is it different from Business Intelligence (BI) and Data Analysis?
  • The lifecycle of Data Science with the help of a use case

The class will focus on breadth and present the topics briefly instead of focusing on a single topic in depth. This will give you the opportunity to sample and apply the basic techniques of data science.

What is Data Science

“An art and science of acquiring knowledge through data”

Using data to gain new insights that you would otherwise have missed

Data Science is all about:-

  • Gathering Data;
  • Acquiring Knowledge; and
  • Using Knowledge to:-
    • Make Decisions;
    • Predict Future;
    • Understand the Past/Present; and
    • Create New Industries/Products.

Why Data Science

  • Empowering management and officers to make better decisions
  • Directing the actions based on trends
  • Challenging the staff to adopt best practices
  • Identifying opportunities
  • Decision making with quantifiable, data-driven evidence
  • Testing these decisions

Who is Data Scientist: Skills Required

  • Maths and Statistics: Algebra, Descriptive and Inferential Statistics, Probability
  • Machine Learning Concepts: Understanding, Libraries Available
  • Database Technology: RDBMS, No SQL, Hadoop
  • Programming Language: R / Python, Spreadsheet, Stata, SPSS, Matlab, SAS
  • Domain Knowledge: Can’t develop this skill by training
  • Presentation Skills: MS Office, R / Python, Communication Skills

Roles in Data Science

  • Data Engineer / Database Administrator (DBA)
  • Data Scientist
  • Big Data Specialist
  • Data Science Manager / Business Executive

Roles in Data Science: Data Engineer / DBA

  • Build Infrastructure
  • May involve in procurement
  • Manage data storage
  • Hardware knowledge (Storage and Computing)
  • Mostly from Computer Science or Computer Engineering

Data Science Training in Lahore Islamabad Pakistan.

Roles in Data Science: Data Scientist

  • Build research questions and Runs experiments
  • Pulls and clean data, Analyze and Communicate
  • Qualification in Statistics, Computer Science, Business

Management and Mathematics etc and knows some ML

  • Usually knows R or Python for programming
  • D3.JS / Tableau for Visualization
  • Knows SQL to pull data from RDBMS

Roles in Data Science: Big Data Specialist

  • Handle real time transactional data
  • Deal with unstructured data
  • Heavy Computer Science and Mathematics skills

Roles in Data Science: Manager

  • Building the data science team
  • Manage data science projects, frame business relevant questions and answers
  • Some sort of data science background plus management Experience

Roles in Data Science: Unicorn

  • No one can be specialist of all
  • Teams can create Unicorn

Our blog on Pre-requisites for Data Science Training

Data Science Process (Iterative Process)

  • Setting the Research Goal: Project Charter: Objective, Timelines, Deliverable etc
  • Retrieving Data: Internal / External, Quality, Access to Data
  • Data Preparation: Data Cleansing, Data Integration, Data Transformation
  • Data Exploration: EDA: Variables Interaction, Distribution, Outliers, Descriptive Stats
  • Data Modeling: Statistics, Machine Learning, Model Selection and Diagnostic
  • Presentation and Automation: Presentation and Research Reports / Automation

What is Machine Learning

“Giving computers the ability to learn from data without explicit “rules” being given by a programmer”

  • Machine Learning (ML) is a sub-discipline of Artificial Intelligence.
  • ML is part of the tool kit, used by Data Scientists.

Data Science should not be thought of as an end all solution to our data woes; it is merely an opinion, a very informed opinion, but an opinion nonetheless. It deserves a seat at the tables.

To book session on Data Science Training or avail our Data Analysis Services call on +92 31 8408828 or email us at

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