LMS

Applied Mastery Program

Fundamentals of Data Science for Business Professionals

This course will introduce you to the fundamentals of data science for business professionals. You will learn the key concepts, methodologies, and tools of data science, and how to apply them to solve real-world business problems. You will gain hands-on experience with data cleaning, exploratory data analysis, data visualization, statistical analysis, and machine learning.

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Estimated Time

24 Weeks

Prerequisites

No need any prior Knowledge or Experience to complete this course

Why choose us?

– High-quality content
– Hands-on projects
– Weekly assessments
– Certificate on completion

Introduction

Welcome to our specialized course in Data Science, In today’s data-driven world, businesses are increasingly relying on data science to make informed decisions, gain competitive advantages, and drive innovation. Data science is a multidisciplinary field that encompasses statistics, computer science, and domain expertise to extract meaningful insights from data. It has become an essential tool for businesses of all sizes and industries.

This comprehensive course in Data Science for Business Professionals will equip you with the necessary skills and knowledge to harness the power of data and drive business success. By the end of this program, you will have gained a thorough understanding of the fundamental concepts of data science and its applications in the business world. You will be able to apply data science methodologies to solve real-world business problems, effectively clean and prepare data for analysis, perform exploratory data analysis and create insightful data visualizations, apply statistical analysis to uncover meaningful patterns in data, build and evaluate machine learning models to make predictive insights, and communicate data-driven insights effectively to stakeholders. This course will transform you into a data-driven business professional, enabling you to make informed decisions that drive business growth and innovation.

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What you will learn

Learn the fundamentals of data analysis, including descriptive statistics, data visualization, and Google Analytics

Gain proficiency in using Tableau to create data visualizations

Understand the different types of marketing metrics and how to calculate them

Use marketing data to answer business questions and make informed decisions

Create marketing dashboards and reports that are clear, concise, and actionable

Develop a deep understanding of the customer journey and how to use data to improve customer experience

Identify and measure the effectiveness of marketing campaigns

Use data to optimize marketing budgets and resources

Course Content

Module 1:Introduction to Data

Lesson 1

Evolution of Data Science

  • Data Science evolved from basic statistics to a sophisticated field integrating machine learning and AI, driven by technological advancements and the exponential growth of data.

Lesson 2

Introduction to Business Intelligence and Business Analytics

  • Defining analysis and analytics
  • Covers the idea of Business Intelligence and Business Analytics, focusing on how data analysis and visualization techniques can inform and enhance business decision-making.

Lesson 3

Introduction to Data

  • Learn about types of data and their characteristics.

Lesson 4

Traditional Data Techniques

  • Understanding the process of making information based in traditional data.
  • Data collection and pre-processing steps in traditional data.
  • Real-world applications.

Lesson 5

Big Data Techniques

  • Defining big data concept.
  • Data collection and pre-processing steps in big data.
  • Real-world applications.

Lesson 6

Business Intelligence (BI) Techniques

  • Defining BI concept.
  • Process of making information for informed decisions.
Module 2: Statistics

Lesson 1

Introduction to Business Statistics

  • Overview of statistics in business.
  • Importance of statistics in decision-making.
  • Descriptive vs. inferential statistics.

Lesson 2

Data Collection, Sampling and Population

  • Data sources and collection methods.
  • Sampling techniques (simple random sampling, stratified sampling, etc.).
  • Sampling bias and errors.
  • Data validation and cleaning.

Lesson 3

Descriptive Statistics

  • Types of Data
  • Levels of Measurement
  • Categorical Variables – Visualization Techniques
  • Numerical Variables – Frequency Distribution Table
  • The Histogram
  • Cross Table and Scatter Plot
  • Mean, median and mode
  • Skewness
  • Variance
  • Standard Deviation and Coefficient of Variation
  • Covariance
  • Correlation Coefficient

Lesson 4

Inferential Statistics Fundamentals

  • Introduction
  • What is Distribution?
  • The Normal Distribution
  • The Standard Normal Distribution
  • Central Limit Theorem
  • Standard error
  • Estimators and Estimates
  • What are Confidence Intervals?
  • Confidence Intervals; Population Variance Known; z-score.
  • T Distribution
  • Confidence Intervals; Population Variance Unknown; t-score
  • Margin of Error
  • Confidence intervals. Two means. Dependent samples

Lesson 5

Hypothesis Testing

  • The Null vs Alternative Hypothesis
  • Rejection Region and Significance Level
  • Type I Error and Type II Error
  • Test for the Mean. Population Variance Known
  • p-value
  • Test for the Mean. Population Variance Unknown
  • Test for the Mean. Dependent Samples
Module 3: Relational Databases and SQL for Data Analysis

Lesson 1

Introduction to Databases

  • Understanding the basic concepts of relational databases, including tables, records, and fields.

Lesson 2

Database Management Systems

  • Overview of DBMS, its role, and importance in managing data within an organization.

Lesson 3

Database Modeling Building Blocks

  • Introducing key components of database modeling, such as entities, attributes, relationships, and data types.

Lesson 4

Relational Database Development and ERD

  • Learning about normalization, entity-relationship diagrams, and designing efficient database schemas.

Lesson 5

Physical Database Development

  • Covering the steps involved in the physical creation of a database, including storage structures, file organization, and optimizing physical layout for performance.

Lesson 6

Basic SQL

  • Become fluent in basic SQL commands including SELECT, FROM, WHERE, and corresponding logical operators. Gain an overview of what you’ll be learning and doing in the course.

Lesson 7

SQL Joins

  • Combine data tables using SQL joins to answer more complex business questions.

Lesson 8

SQL Aggregations

  • Aggregate data in SQL including COUNT, SUM, MIN, and MAX.
  • Write CASE and DATE functions, as well as work with NULL values.

Lesson 9

Indexing and Performance Tuning

  • Learning about indexing strategies and how to optimize query performance.

Lesson 10

Stored Procedures and Triggers

  • Introduction to writing stored procedures and triggers for automated data processing.
Module 4: Python Programming

Lesson 1

Why Python Programming

  • Gain an overview of what you’ll be learning and doing in the course
  • Understand why you should learn programming with Python.

Lesson 2

Data Types & Operators

  • Represent data using Python’s data types: integers, floats, Booleans, strings, lists, tuples, sets, dictionaries, compound data structures.
  • Perform computations and create logical statements using Python’s operators: arithmetic, assignment, comparison, logical, membership, and identity.
  • Declare, assign, and reassign values using Python variables.
  • Modify values using built-in functions and methods.
  • Practice whitespace and style guidelines.

Lesson 3

Control Flow

  • Write conditional expressions using if statements and Boolean expressions to add decision making to your Python programs.
  • Use for and while loops along with useful built-in functions to iterate over and manipulate lists, sets, and dictionaries.
  • Skip iterations in loops using break and continue.
  • Condense for loops to create lists efficiently with list comprehensions.

Lesson 4

Functions

  • Define your own custom functions.
  • Create and reference variables using the appropriate scope.
  • Add documentation to functions using docstrings.
  • Define lambda expressions to quickly create anonymous functions.
  • Use iterators and generators to create streams of data.

Lesson 5

Pandas

  • Create, access, and modify the main objects in Pandas, Series, and DataFrames.
  • Perform arithmetic operations on Series and DataFrames.
  • Load data into a DataFrame.
  • Deal with Not a Number (NaN) values.

Lesson 6

Numpy

  • Create, access, modify, and sort multidimensional NumPy arrays (ndarrays).
  • Load and save ndarrays.
  • Use slicing, boolean indexing, and set operations to select or change subsets of an ndarray.
  • Understand the difference between a view and a copy of ndarray.
  • Perform element-wise operations on ndarrays.
  • Use broadcasting to perform operations on ndarrays of different sizes.

Lesson 7

Matplotlib

  • Create basic plots using Matplotlib.
  • Customize plots with different styles and properties.
  • Create subplots and multiple plots.
  • Plot data from Pandas DataFrames.

Lesson 8

Data Manipulation with Pandas

  • Clean and preprocess data using Pandas.
  • Handle missing data and outliers.
  • Aggregate and transform data.
  • Merge, join, and concatenate data.
Module 5: Introduction to Version Control

Lesson 1

Shell Workshop

  • The Unix shell is a powerful tool for developers of all sorts. Get a quick introduction to the basics of using it on your computer.

Lesson 2

Purpose & Terminology

  • Learn why developers use version control and discover ways you use version control in your daily life.
  • Get an overview of essential Git vocabulary.
  • Configure Git using the command line.

Lesson 3

Create a Git Repo

  • Create your first Git repository with git init.
  • Copy an existing Git repository with git clone.
  • Review the current state of a repository with the powerful git status.

Lesson 4

Review a Repo’s History

  • Review a repo’s commit history with git log.
  • Customize git log’s output using command line flags in order to reveal more (or less) information about each commit.
  • Use the git show command to display just one commit.

Lesson 5

Add Commits to a Repo

  • Master the Git workflow and make commits to an example project.
  • Use git diff to identify what parts of a file have been changed in a commit.
  • Learn how to mark files as “untracked” using .gitignore.

Lesson 6

Tagging, Branching & Merging

  • Explore tagging, branching, and merging.
  • Organize your commits with tags and branches.
  • Jump to particular tags and branches using git checkout.
  • Learn how to merge together changes on different branches and crush those pesky merge conflicts.

Lesson 7

Undoing Changes

  • Learn how and when to edit or delete an existing commit.
  • Use git commit’s -amend flag to alter the last commit.
  • Use git reset and git revert to undo and erase commits.
Module 6: Final Project

Applying the acquired knowledge and skills to solve a real-world problem.

Our Lecturers

Nilantha Prasad 

Lecturer @ APIBA

A BSc degree holder in Business Information Systems is visiting the Institute of Chartered Accountants of Sri Lanka. They have previously worked as a lecturer at the Faculty of Management Studies and Commerce, University of Sri Jayewardenepura.

Mr. Chandika Witharana

Lecturer @ APIBA

Lecturer in Software Engineering & Business Intelligence at the Faculty of Management Studies and Commerce University of Sri Jayewardenepura from 2014 – 2022. Chief Executive Officer & Founder at APIBA. Chief Executive Officer & Founder at Xenosys Software solutions.  

Regular Course Fee: LKR. 44,500

Early Bird offer: LKR.37,800

Early Bird offer is valid until 2024 March 15
3-month installment payment plan available

Course Schedule: 2024-03-18
At 8:00pm onward  

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Assessment

100% compulsory to pass the assessment at the end of the program

(Mark should be at least 50% or higher.)

Student Support
Learning Management Systems (LMS) or Contact Coordinator of the course
Mode of Delivery
100% conducted live with access to recordings

Benefits of learning from us

High-quality content

8+ hands-on projects

Weekly assessments

Certificate on completion

“I’m extremely glad I signed up for the program. I definitely got what I wanted from the program and strongly recommend it.”

Javier R. Olaechea Financial Data Analyst

“I’m extremely glad I signed up for the program. I definitely got what I wanted from the program and strongly recommend it.”

Javier R. Olaechea Financial Data Analyst

“I’m extremely glad I signed up for the program. I definitely got what I wanted from the program and strongly recommend it.”

Javier R. Olaechea Financial Data Analyst

Kickstart your learning experience with APIBA.

Register for free now