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## Data Science Syllabus

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Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured.

- Introduction to 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

- 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

- 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

- 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

- 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+)

- 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
- Probability Terminology
- Probability Distributions

- Bubble Chart
- Sparklines
- Waterfall chart
- Box Plot
- Line Charts
- Frequency Chart
- Bimodal & Multimodal Histograms
- Histograms
- Scatter Plot
- Pie Chart
- Bar Graph
- Line Graph

- 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

- 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
- Noise and Model Complexity
- Triple Trade-Off
- Association Rules
- Association Measures

- 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
- Concept of odds
- 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

- 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

- 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

- 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

- 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 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

- Clustering using Tableau
- Time series analysis using Tableau
- Simple Linear Regression using Tableau

- Integrating R code with Tableau
- Creating statistical model with dynamic inputs
- Visualizing R output in Tableau
- Case Study 1- Real time project with Twitter Data Analytics
- Case Study 2- Real time project with Google Finance
- Case Study 3- Real time project with IMDB Website

Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured.
Data science is related to computer science, but is a separate field. Computer science involves creating programs and algorithms to record and process data, while data science covers any type of data analysis, which may or may not use computers. Data science is more closely related to the mathematics field of Statistics, which includes the collection, organization, analysis, and presentation of data.
Because of the large amounts of data modern companies and organizations maintain, data science has become an integral part of IT. For example, a company that has petabytes of user data may use data science to develop effective ways to store, manage, and analyze the data. The company may use the scientific method to run tests and extract results that can provide meaningful insights about their users.

Basic computer knowledge, any data related experience will be advantageous.

- Any graduate/post graduate or students in final stages of graduation + People willing to align careers in analytics
- Team leaders working with data and often need basic data analysis
- Engineers looking for career opportunities in IT/ITES industry
- Management students looking for strategic positions
- People already working with huge datasets
- Hadoop Professionals
- CA, CS, CFA

DINS Infotech offers Data science course, on Regular and Weekend basis.

Online or Classroom training available.

For more details contact on +91-992-375-5189

Online or Classroom training available.

For more details contact on +91-992-375-5189

- Small batch size
- Expert faculty
- Job Assistance for modular course
- Job Guaranteed for Career courses
- Practical oriented training

- Networking
- Hardware & Networking
- CCNA
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- Ethical Hacking

- Microsoft
- MCSA
- Azure
- Server Administration
- Powershell

- Linux
- Amazon AWS
- RHCE

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