Data Science Training Syllabus
Our Data Science Course Syllabus specially designed and covers the key concepts from such as Python Programming, R Language, Statistics, Probability, Linear Algebra, Data Preparation, ETL, Machine Learning, and Visualization Concepts with complete hands-on experiences on real-time projects. After the course completion, one can be able to have a detailed understanding of data science concepts along with extraordinary guidance through our updated Data Science Interview Questions and Answers on our site. Our Course Syllabus covers the following major concepts in our Data Science Training Center in Chennai.
Our Data Science Syllabus contains programming languages like R, Python, Scala, Joomla, SAS, and Data Visualization tools like Tableau, Power BI, QlikView, and Machine Learning algorithms such as linear regression, logistic regression, clustering, and big data technologies like Spark, Hadoop, AWS, Azure, and risk monitoring concepts in our Data Science Training Institute in Chennai with 100% Placement Support.
Data Science Training Syllabus
Introduction to Data Science
First and foremost, we cover the following topics in the syllabus:
- Overview of Data Analytics
- Introduction to Business Analytics
- Understanding Business Applications
- Data types and data Models
- Type of Business Analytics
- Evolution of Analytics
- Data Science Components
- Data Scientist Skillset
- Univariate Data Analysis
- Introduction to Sampling
Basic Operations in R Programming
- Introduction to R programming
- Types of Objects in R
- Naming standards in R
- Creating Objects in R
- Data Structure in R
- Matrix, Data Frame, String, Vectors
- Understanding Vectors & Data input in R
- Lists, Data Elements
- Creating Data Files using R
Data Handling in R Programming
- Basic Operations in R – Expressions, Constant Values, Arithmetic, Function Calls, Symbols
- Sub-setting Data
- Selecting (Keeping) Variables
- Excluding (Dropping) Variables
- Selecting Observations and Selection using Subset Function
- Merging Data
- Sorting Data
- Adding Rows
- Visualization using R
- Data Type Conversion
- Built-In Numeric Functions
- Built-In Character Functions
- User Built Functions
- Control Structures
- Loop Functions
Introduction to Statistics
- Basic Statistics
- Measure of central tendency
- Types of Distributions
- Anova
- F-Test
- Central Limit Theorem & applications
- Types of variables
- Relationships between variables
- Central Tendency
- Measures of Central Tendency
- Kurtosis
- Skewness
- Arithmetic Mean / Average
- Merits & Demerits of Arithmetic Mean
- Mode, Merits & Demerits of Mode
- Median, Merits & Demerits of Median
- Range
- Concept of Quantiles, Quartiles, percentile
- Standard Deviation
- Variance
- Calculate Variance
- Covariance
- Correlation
Introduction to Statistics – 2
- Hypothesis Testing
- Multiple Linear Regression
- Logistic Regression
- Market Basket Analysis
- Clustering (Hierarchical Clustering & K-means Clustering)
- Classification (Decision Trees)
- Time Series Analysis (Simple Moving Average, Exponential smoothing, ARIMA+)
Introduction to Probability
- Standard Normal Distribution
- Normal Distribution
- Geometric Distribution
- Poisson Distribution
- Binomial Distribution
- Parameters vs. Statistics
- Probability Mass Function
- Random Variable
- Conditional Probability and Independence
- Unions and Intersections
- Finding Probability of dataset
- Terminology in Probability
- Probability Distributions
Data Visualization Techniques
- Bubble Chart
- Sparklines
- Waterfall chart
- Box Plot
- Line Charts
- Frequency Chart
- Bimodal & Multimodal Histograms
- Histograms
- Scatter Plot
- Pie Chart
- Bar Graph
- Line Graph
Introduction to Machine Learning
- Overview & Terminologies
- What is Machine Learning?
- Why Learn?
- When is Learning required?
- Data Mining
- Application Areas and Roles
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement learning
Machine Learning Concepts & Terminologies
Steps in developing a Machine Learning application
- Key tasks of Machine Learning
- Modelling Terminologies
- Learning a Class from Examples
- Probability and Inference
- PAC (Probably Approximately Correct) Learning
- Noise
- Complexity-Noise and Model
- Triple Trade-Off
- Association Rules
- Association Measures
Regression Techniques
- Concept of Regression
- Best Fitting line
- Simple Linear Regression
- Building regression models using excel
- Coefficient of determination (R- Squared)
- Multiple Linear Regression
- Assumptions of Linear Regression
- Variable transformation
- Reading coefficients in MLR
- Multicollinearity
- VIF
- Methods of building Linear regression model in R
- Model validation techniques
- Cooks Distance
- Q-Q Plot
- Durbin- Watson Test
- Kolmogorov-Smirnof Test
- Homoskedasticity of error terms
- Logistic Regression
- Applications of logistic regression
- Odds concept
- Concept of Odds Ratio
- Derivation of logistic regression equation
- Interpretation of logistic regression output
- Model building for logistic regression
- Model validations
- Confusion Matrix
- Concept of ROC/AOC Curve
- KS Test
Market Basket Analysis
- Applications of Market Basket Analysis
- What is association Rules
- Overview of Apriori algorithm
- Key terminologies in MBA
- Support
- Confidence
- Lift
- Model building for MBA
- Transforming sales data to suit MBA
- MBA Rule selection
- Ensemble modelling applications using MBA
Time Series Analysis (Forecasting)
- Model building using ARIMA, ARIMAX, SARIMAX
- Data De-trending & data differencing
- KPSS Test
- Dickey Fuller Test
- Concept of stationarity
- Model building using exponential smoothing
- Model building using simple moving average
- Time series analysis techniques
- Components of time series
- Prerequisites for time series analysis
- Concept of Time series data
- Applications of Forecasting
Decision Trees using R
- Understanding the Concept
- Internal decision nodes
- Terminal leaves.
- Tree induction: Construction of the tree
- Classification Trees
- Entropy
- Selecting Attribute
- Information Gain
- Partially learned tree
- Overfitting
- Causes for over fitting
- Overfitting Prevention (Pruning) Methods
- Reduced Error Pruning
- Decision trees – Advantages & Drawbacks
- Ensemble Models
K Means Clustering
- Parametric Methods Recap
- Clustering
- Direct Clustering Method
- Mixture densities
- Classes v/s Clusters
- Hierarchical Clustering
- Dendogram interpretation
- Non-Hierarchical Clustering
- K-Means
- Distance Metrics
- K-Means Algorithm
- K-Means Objective
- Color Quantization
- Vector Quantization
Tableau Analytics
- Tableau Introduction
- Data connection to Tableau
- Calculated fields, hierarchy, parameters, sets, groups in Tableau
- Various visualizations Techniques in Tableau
- Map based visualization using Tableau
- Reference Lines
- Adding Totals, sub totals, Captions
- Advanced Formatting Options
- Using Combined Field
- Show Filter & Use various filter options
- Data Sorting
- Create Combined Field
- Table Calculations
- Creating Tableau Dashboard
- Action Filters
- Creating Story using Tableau
Analytics using Tableau
- Clustering using Tableau
- Time series analysis using Tableau
- Simple Linear Regression using Tableau
R integration in Tableau
- Integrating R code with Tableau
- Creating statistical model with dynamic inputs
- Visualizing R output in Tableau
- Case Studies
- 1- Real time project with Twitter Data Analytics
- 2- Real time project with Google Finance
- 3- Real time project with IMDB Website