DATA SCIENCE COURSE IN VIENNA, AUSTRIA

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
  

Data Science is the dedicated discipline focused on extracting valuable insights and knowledge from extensive sets of both structured and unstructured data. It employs an array of techniques, algorithms, and systems to analyze, interpret, and present data in a meaningful manner.

The operational process of Data Science entails the systematic collection, cleaning, and analysis of data to unveil significant patterns and trends. Utilizing statistical models, machine learning algorithms, and data visualization techniques, informed decisions are made based on the discovered insights.

Data Science finds practical applications in predictive analytics, fraud detection, recommendation systems, sentiment analysis, and the optimization of business processes across diverse industries, showcasing its versatility and significance.

Critical components of a Data Science pipeline encompass data collection, data cleaning, exploratory data analysis (EDA), feature engineering, model training, model evaluation, and deployment. These stages collectively contribute to the holistic process of extracting insights from data.

Python and R emerge as frequently employed programming languages in Data Science. Their popularity stems from the availability of extensive libraries and frameworks facilitating tasks such as data manipulation, analysis, and the implementation of machine learning algorithms.

Machine learning plays a pivotal role in Data Science by empowering systems to autonomously discern patterns from data, enabling predictions and decisions without explicit programming. This capability enhances the extraction of valuable insights from complex datasets.

The relationship between Big Data and Data Science is intimate, with Data Science being instrumental in handling and analyzing extensive datasets that conventional data processing tools may struggle to manage. The application of Data Science methodologies and algorithms becomes crucial for extracting meaningful information from the vast expanse of Big Data.

Data Science finds practical application in diverse sectors such as healthcare, finance, marketing, and manufacturing. Its role extends to optimizing operations, refining decision-making processes, and enhancing overall business performance within these industries.

While Data Science encompasses a broader spectrum of activities, including data cleaning, exploration, and visualization, machine learning specifically focuses on crafting algorithms that empower systems to autonomously learn patterns and make predictions.

Eligible candidates for Data Science certification courses come from diverse backgrounds, including IT professionals, statisticians, analysts, and business experts. A foundational understanding of statistics and programming proves beneficial for individuals venturing into the realm of Data Science.