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.
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.
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.
Introduction to Data Learn about types of data and their characteristics.
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.
Big Data Techniques : Defining big data concept. Data collection and pre-processing steps in big data. Real-world applications.
Business Intelligence (BI) Techniques : Defining BI concept. Process of making information for informed decisions.
Introduction to Business Statistics : Overview of statistics in business. Importance of statistics in decision-making. Descriptive vs. inferential statistics.
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.
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
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
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
Introduction to Databases : Understanding the basic concepts of relational databases, including tables, records, and fields.
Database Management Systems : Overview of DBMS, its role, and importance in managing data within an organization.
Database Modeling Building Blocks : Introducing key components of database modeling, such as entities, attributes, relationships, and data types.
Relational Database Development and ERD : Learning about normalization, entity-relationship diagrams, and designing efficient database schemas.
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.
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.
SQL Joins : Combine data tables using SQL joins to answer more complex business questions.
SQL Aggregations : Aggregate data in SQL including COUNT, SUM, MIN, and MAX. Write CASE and DATE functions, as well as work with NULL values
Indexing and Performance Tuning : Learning about indexing strategies and how to optimize query performance.
Stored Procedures and Triggers : Introduction to writing stored procedures and triggers for automated data processing.
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.
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.
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.
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.
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.
Matplotlib : Create basic plots using Matplotlib. Customize plots with different styles and properties. Create subplots and multiple plots. Plot data from Pandas DataFrames.
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.
Data Manipulation with Pandas Clean and preprocess data using Pandas. Handle missing data and outliers. Aggregate and transform data. Merge, join, and concatenate data.
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.
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.
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.
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.
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.
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.
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.
Applying the acquired knowledge and skills to solve a real-world problem.
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At , Asia Pacific Institute of Business Analytics – APIBA Institute we are proud to announce that our courses are accredited by CPD UK, one of the leading independent Continuing Professional Development (CPD) accreditation institutions. Choosing a CPD-accredited course is not just about enhancing your resume; it’s about investing in your future and unlocking your full potential. Here’s why:
CPD UK accreditation means that our courses meet the highest standards of excellence and relevance. This seal of approval is recognized globally, ensuring that the skills and knowledge you gain are not only applicable but also respected across various industries. When employers see CPD UK accreditation, they know you’ve been trained to the standards that matter.
The importance of CPD cannot be overstated in today’s ever-evolving job market. Engaging in Continuous Professional Development ensures that both academic and practical qualifications do not become outdated, allowing individuals to continually ‘up skill’ or ‘re-skill’ themselves, regardless of occupation, age, or educational level. This approach to lifelong learning opens doors to new opportunities and career paths that you may never have considered.
A CPD certificate is not just a document; it’s a testament to your commitment to professional development and excellence. CPD UK certificates are highly valued by employers and professionals alike for their integrity and value. They serve as evidence of your dedication to continuous improvement and your capability to adapt and excel in your chosen field. This credibility is paramount in building a distinguished career.
In a world where the job market is more competitive than ever, having a CPD-accredited course under your belt gives you a significant advantage. It demonstrates to potential employers that you are proactive, dedicated, and equipped with the latest skills and knowledge. This can be the key factor that sets you apart from other candidates.
By enrolling in our CPD-accredited courses, you’re not just signing up for education; you’re joining a community committed to excellence and continuous growth. We don’t just prepare you for the job market; we prepare you to lead in your field. Our courses are designed with your future in mind, blending practical skills with cutting-edge theory, all underpinned by the rigorous standards of CPD UK.
Regular Course Fee: LKR. 44,500
Early bird offer: LKR. 37,800
Early bird offer valid until 2024 March 15
Next Intake Starting on : 2024.03.18 on 8.00 p.m.
3-month installment payment plan available
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All the things You Need to know about Data science Top 5 in-demand tech skills We Care About Quality Content. We Never Wanted To Be A Big Publishing House: Our Team Is Small, But It’s A Truly Wonderful Team Of People Who Really Care About What They Do. Passionate And Dedicated. Honest And Respectful. Professional … Enhancing Career Growth: Why Data Science is Needed for Banking Professionals.
All the things You Need to know about Data science We Care About Quality Content. We Never Wanted To Be A Big Publishing House: Our Team Is Small, But It’s A Truly Wonderful Team Of People Who Really Care About What They Do. Passionate And Dedicated. Honest And Respectful. Professional But Informal. Quirky And Personal. … Enhancing Career Growth: Why Data Science is Needed for IT Professionals.
All the things You Need to know about Data science We Care About Quality Content. We Never Wanted To Be A Big Publishing House: Our Team Is Small, But It’s A Truly Wonderful Team Of People Who Really Care About What They Do. Passionate And Dedicated. Honest And Respectful. Professional But Informal. Quirky And Personal. … Enhancing Career Growth: Why Data Science is Needed for Management Professionals.
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