Data Science

Python, Data Science, Data Analysis, Data Wrangling, SQL, Machine Learning, Prediction Algorithms, Data Visualization

Python 

Introduction to Python and IDEs – The basics of the python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc. 

• Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling etc.

• Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.

Excel Fundamentals 

• Reading the Data, Referencing in formulas , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering 

• Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security 

• IF conditions, loops, Debugging, etc.

Excel For Data Analytics 

• Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.  

Data Visualization with Excel

• Charts, Pie charts, Scatter and bubble charts

• Bar charts, Column charts, Line charts, Maps

• Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

Classification Problems using Excel

• Binary Classification Problems, Confusion Matrix, AUC and ROC curve 

• Multiple Classification Problems  

Information Measure in Excel

• Probability, Entropy, Dependence 

• Mutual Information 

Regression Problems Using Excel

• Standardization, Normalization, Probability Distributions 

• Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation

• Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression

SQL Basics – 

• Fundamentals of Structured Query Language

• SQL Tables, Joins, Variables 

Advanced SQL –  

• SQL Functions, Subqueries, Rules, Views

• Nested Queries, string functions, pattern matching

• Mathematical functions, Date-time functions, etc. 

Deep Dive into User Defined Functions

• Types of UDFs, Inline table value, multi-statement table. 

• Stored procedures, rank function, SQL ROLLUP, etc.

SQL Optimization and Performance

• Record grouping, searching, sorting, etc. 

• Clustered indexes, common table expressions.

Extract Transform Load

• Web Scraping, Interacting with APIs

Data Handling with NumPy

• NumPy Arrays, CRUD Operations, etc.

• Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.

Data Manipulation Using Pandas

• Loading the data, dataframes, series, CRUD operations, splitting the data, etc.

Data Preprocessing

• Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc.

• Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross validation techniques, train-test split, etc.

Data Visualization

• Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc. with Python matplotlib.

• Regression plots, categorical plots, area plots, etc, with Python seaborn.

Descriptive Statistics – 

• Measure of central tendency, measure of spread, five points summary, etc. 

Probability 

• Probability Distributions, bayes theorem, central limit theorem. 

Inferential Statistics –  

• Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

Introduction to Machine learning 

• Supervised, Unsupervised learning.

• Introduction to scikit-learn, Keras, etc.

Regression 

• Introduction classification problems, Identification of a regression problem, dependent and independent variables.

• How to train the model in a regression problem.

• How to evaluate the model for a regression problem.

• How to optimize the efficiency of the regression model.

Classification 

• Introduction to classification problems, Identification of a classification problem, dependent and independent variables.

• How to train the model in a classification problem.

• How to evaluate the model for a classification problem.

• How to optimize the efficiency of the classification model.

Clustering 

• Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.

• How to train the model in a clustering problem.

• How to evaluate the model for a clustering problem.

• How to optimize the efficiency of the clustering model.

Supervised Learning 

• Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.

• Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.

• Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.

Power BI Basics

• Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI.

• Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.

DAX 

• Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

• Students and Graduates
• Working Professionals & Analysts
• Career Switchers into Data & AI fields

• Analyze and visualize structured data
• Build machine learning models using Python
• Clean and transform data using SQL and Excel
• Create professional dashboards in Power BI

• Data Analyst
• Business Intelligence Specialist
• Machine Learning Engineer
• Data Scientist

• Industry-Relevant Tools: Learn with Python, SQL, Power BI, and Excel
• Expert Trainers: Learn from professionals with real-world experience
• KHDA-Certified Certificate: Boost your global employability
• Flexible Learning: Choose between online or in-person training

• Basic knowledge of computers or programming
• Curiosity about data, problem solving, or business intelligence