DATA SCIENCE COURSE IN DOHA, QATAR

MODULE 1: DATA SCIENCE COURSE INTRODUCTION 

• CDS Course Introduction
• 3 Phase Learning
• Learning Resources
• Assessments & Certification Exams
• DataMites Mobile App
• Support Channels

MODULE 2: DATA SCIENCE ESSENTIALS 

• Introduction to Data Science
• Evolution of Data Science
• Data Science Terminologies
• Data Science vs AI/Machine Learning
• Data Science vs Analytics
  

MODULE 3: DATA SCIENCE DEMO 

• Business Requirement: Use Case
• Data Preparation
• Machine learning Model building
• Prediction with ML model
• Delivering Business Value
  

MODULE 4: ANALYTICS CLASSIFICATION 

• Types of Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics
  

MODULE 5: DATA SCIENCE AND RELATED FIELDS

• Introduction to AI
• Introduction to Computer Vision
• Introduction to Natural Language Processing
• Introduction to Reinforcement Learning
• Introduction to GAN
• Introduction to  Generative Passive Models
  

MODULE 6: DATA SCIENCE ROLES & WORKFLOW

• Data Science Project workflow
• Roles: Data Engineer, Data Scientist, ML Engineer and MLOps Engineer
• Data Science Project stages

MODULE 7: MACHINE LEARNING INTRODUCTION

• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
  

MODULE 8: DATA SCIENCE INDUSTRY APPLICATIONS 

• Data Science in Finance and Banking
• Data Science in Retail
• Data Science in Health Care
• Data Science in Logistics and Supply Chain
• Data Science in Technology Industry
• Data Science in Manufacturing
• Data Science in Agriculture
  

MODULE 1: PYTHON BASICS 

• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity

MODULE 2: PYTHON CONTROL STATEMENTS 

• IF Conditional statement
• IF-ELSE • NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements
  

MODULE 3: PYTHON DATA STRUCTURES 

• Basic data structure in python
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions
  

MODULE 4: PYTHON FUNCTIONS 

• Functions basics
• Function Parameter passing
• Iterators
• Generator functions
• Lambda functions
• Map, reduce, filter functions
  

MODULE 5: PYTHON NUMPY PACKAGE 

• NumPy Introduction
• Array – Data Structure
• Core Numpy functions
• Matrix Operations
  

MODULE 6: PYTHON PANDASPACKAGE

• Pandasfunctions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis

MODULE 1: OVERVIEW OF STATISTICS 

• Descriptive And Inferential Statistics
• Basic Terms Of Statistics
• Types Of Data
  

MODULE 2: HARNESSING DATA 

• Random Sampling
• Sampling With Replacement And Without Replacement
• Cochran's  Minimum Sample Size
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
• Biased Random Sampling Methods
• Sampling Error
• Methods Of Collecting Data
  

MODULE 3: EXPLORATORY DATA ANALYSIS 

• Exploratory Data Analysis Introduction
• Measures Of Central Tendencies: Mean, Median And Mode
• Measures Of Central Tendencies: Range, Variance And Standard Deviation
• Data Distribution Plot: Histogram
• Normal Distribution
• Z Value / Standard Value
• Empherical Rule  and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance
  

MODULE 4: HYPOTHESIS TESTING 

• Hypothesis Testing Introduction
• P- Value, Confidence Interval
• Parametric Hypothesis Testing Methods
• Hypothesis Testing Errors : Type I And Type Ii
• One Sample T-test
• Two Sample Independent T-test
• Two Sample Relation T-test
• One Way Anova Test
  

MODULE 5: CORRELATION AND REGRESSION 

• Correlation Introduction
• Direct/Positive Correlation
• Indirect/Negative Correlation
• Regression
• Choosing Right Method
  

MODULE 1: MACHINE LEARNING INTRODUCTION 

• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
  

MODULE 2: PYTHON NUMPY & PANDAS PACKAGE 

• NumPy & Pandas functions
• Array – Data Structure
• Core Numpy functions
• Matrix Operations
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis
  

MODULE 3: VISUALIZATION WITH PYTHON 

• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
• Advanced Python Data Visualizations
  

MODULE 4: ML ALGO: LINEAR REGRESSION

• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python
  

MODULE 5: ML ALGO: KNN 

• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python
  

MODULE 6: ML ALGO: LOGISTIC REGRESSION 

• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python
  

MODULE 7: PRINCIPLE COMPONENT ANALYSIS (PCA) 

• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python
  

MODULE 8: ML ALGO: K MEANS CLUSTERING 

• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works: K Means theory
• Modeling in Python
  

MODULE 1: MACHINE LEARNING INTRODUCTION 

• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
  

MODULE 2: ML ALGO: LINEAR REGRESSSION 

• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python
  

MODULE 3: ML ALGO: LOGISTIC REGRESSION 

• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python
  

MODULE 4: ML ALGO: KNN 

• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python
  

MODULE 5: ML ALGO: K MEANS CLUSTERING 

• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Modeling in Python
  

MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA) 

• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python
  

MODULE 7: ML ALGO: DECISION TREE 

• Random Forest Ensemble technique
• How it works: Bagging Theory
• Modeling and Evaluation in Python
  

MODULE 8 : ML ALGO: NAÏVE BAYES 

• Introduction to Naive Bayes
• How it works: Bayes' Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python
  

MODULE 9: GRADIENT BOOSTING, XGBOOST 

• Introduction to Boosting and XGBoost
• How it works: weak learners' concept
• Modeling and Evaluation of in Python
  

MODULE 10: ML ALGO: SUPPORT VECTOR MACHINE  (SVM) 

• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python
  

MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN) 

• Introduction to ANN
• How It Works: Back prop, Gradient Descent
• Modeling and Evaluation of ANN in Python
  

MODULE 12: ADVANCED ML CONCEPTS 

• Adv Metrics (Roc_Auc, R2, Precision, Recall)
• K-Fold Cross-validation
• Grid And Randomized Search CV In Sklearn
• Imbalanced Data Set: Smote Technique
• Feature Selection Techniques
  

MODULE 1: TIME SERIES FORECASTING - ARIMA 

• What is Time Series?
• Trend, Seasonality, cyclical and random
• Autoregressive Model (AR)
• Moving Average Model (MA)
• Stationarity of Time Series
• ARIMA Model
• Autocorrelation and AIC 

MODULE 2: FEATURE ENGINEERING 

• Introduction to Features Engineering
• Transforming Predictors
• Feature Selection methods
• Backward elimination technique
• Feature importance from ML modeling
  

MODULE 3: SENTIMENT ANALYSIS 

• Introduction to Sentiment Analysis
• Python packages: TextBlob, NLTK
• Case study: Twitter Live Sentiment Analysis
  

MODULE 4: REGULAR EXPRESSIONS WITH PYTHON 

• Regex Introduction
• Regex codes
• Text extraction with Python Regex
  

MODULE 5: ML MODEL DEPLOYMENT WITH FLASK

• Introduction to Flask
• URL and App routing
• Flask application – ML Model deployment

MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL 

• MS Excel core Functions
• Pivot Table
• Advanced Functions (VLOOKUP, INDIRECT..)
• Linear Regression with EXCEL
• Goal Seek Analysis
• Data Table
• Solving Data Equation with EXCEL
• Monte Carlo Simulation with MS EXCEL
  

MODULE 7: AWS CLOUD FOR DATA SCIENCE

• Introduction of cloud
• Difference between GCC, Azure,AWS
• AWS Service ( EC2 and S3 service)
• AWS Service (AMI), AWS Service (RDS)
• AWS Service (IAM), AWS (Athena service)
• AWS (EMR), AWS, AWS (Redshift)
• ML Modeling with AWS Sage Maker 

MODULE 8: AZURE FOR DATA SCIENCE 

• Introduction to AZURE ML studio
• Data Pipeline and ML modeling with Azure
  

MODULE 1: DATABASE INTRODUCTION 

• DATABASE Overview
• Key concepts of database management
• CRUD Operations
• Relational Database Management System
• RDBMS vs No-SQL (Document DB)
  

MODULE 2: SQL BASICS 

• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL  workbench installation
• Comments
• import and export dataset
  

MODULE 3: DATA TYPES AND CONSTRAINTS 

• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment
  

MODULE 4: DATABASES AND TABLES (MySQL) 

• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete  table records
• Create new table from existing data types
• Insert into, Update records
• Alter table
  

MODULE 5: SQL JOINS 

• Inner join
• Outer join
• Left join
• Right join
• Cross join
• Self join
  

MODULE 6: SQL COMMANDS AND CLAUSES 

• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries
  

MODULE 7 : DOCUMENT DB/NO-SQL DB 

• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
• MongoDB data management
  

MODULE 1: GIT  INTRODUCTION 

• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture

MODULE 2: GIT REPOSITORY and GitHub 

• Git Repo Introduction
• Create New Repo with Init command
• Copying existing repo
• Git user and remote node
• Git Status and rebase
• Review Repo History
• GitHub Cloud Remote Repo
  

MODULE 3: COMMITS, PULL, FETCH AND PUSH 

• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo
  

MODULE 4: TAGGING, BRANCHING AND MERGING 

• Organize code with branches
• Checkout branch
• Merge branches
  

MODULE 5: UNDOING CHANGES 

• Editing Commits
• Commit command Amend flag
• Git reset and revert
  

MODULE 6: GIT WITH GITHUB AND BITBUCKET 

• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
• Bitbucket Git account

MODULE 1: BIG DATA INTRODUCTION 

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction
  

MODULE 2 : HDFS AND MAP REDUCE 

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing  stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
• Hands-on Map Reduce task
  

MODULE 3: PYSPARK FOUNDATION 

• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs
  

MODULE 4: SPARK SQL and HADOOP HIVE 

• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
• Working with Spark SQL Query Language
  

MODULE 5 : MACHINE LEARNING WITH SPARK ML 

• Introduction to MLlib Various ML algorithms supported by MLib
• ML model with Spark ML
• Linear regression
• logistic regression
• Random forest
  

MODULE 6: KAFKA and Spark 

• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations
  

MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION 

• What Is Business Intelligence (BI)?
• What Bi Is The Core Of Business Decisions?
• BI Evolution
• Business Intelligence Vs Business Analytics
• Data Driven Decisions With Bi Tools
• The Crisp-Dm Methodology
  

MODULE 2: BI WITH TABLEAU: INTRODUCTION

• The Tableau Interface
• Tableau Workbook, Sheets And Dashboards
• Filter Shelf, Rows And Columns
• Dimensions And Measures
• Distributing And Publishing
  

MODULE 3 : TABLEAU: CONNECTING TO DATA SOURCE 

• Connecting To Data File , Database Servers
• Managing Fields
• Managing Extracts
• Saving And Publishing Data Sources
• Data Prep With Text And Excel Files
• Join Types With Union
• Cross-Database Joins
• Data Blending
• Connecting To Pdfs
  

MODULE 4: TABLEAU : BUSINESS INSIGHTS 

• Getting Started With Visual Analytics
• Drill Down And Hierarchies
• Sorting & Grouping
• Creating And Working Sets
• Using The Filter Shelf
• Interactive Filters
• Parameters
• The Formatting Pane
• Trend Lines & Reference Lines
• Forecasting
• Clustering
  

MODULE 5: DASHBOARDS, STORIES AND PAGES 

• Dashboards And Stories Introduction
• Building A Dashboard
• Dashboard Objects
• Dashboard Formatting
• Dashboard Interactivity Using Actions
• Story Points
• Animation With Pages
  

MODULE 6: BI WITH POWER-BI 

• Power BI basics
• Basics Visualizations
• Business Insights with Power BI
  

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