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**Prerequisite: **No expertise is required; however basic knowledge High School Mathematics and Statistics will be required.

Introduction to Data Science

- What is data science
- Different fields of data science
- The ven diagram for data science
- Tools of data science
- Introduction to R for data science

Introduction to Python programming

- Introduction to Python language
- Environmental setup for Python, Anaconda, Jupyter, Spider etc
- Data types of Python, numbers, string
- If, elif, Loops in python
- Functions in python
- Lambda function
- Modules of Python
- Import a module, call a function of a module

Object oriented Python

- Introduction to Object oriented language
- Class, object
- Create class and object in python
- Data hiding, inheritance

String manipulation and collections in python

- Creating and accessing strings
- Basic functions on strings
- Indexing and slicing on string
- Strings methods
- List and its methods
- Accessing lists
- Tuple, set, dictionary and their methods
- Indexing, slicing on list, tuple, set , dictionary
- List comprehension and its uses

Introduction Numeric and Data Analysis Modules of Python

- Introduction to Numpy
- Creating 1D and 2D Numpy array
- Slicing and indexing of Numpy Array
- Introduction to Pandas
- Series and DataFrame
- Different functions on dataframe
- Read external dataset using Pandas

Handling Data – Data Wrangling

- What is Data Wrangling
- Prepare data for use
- Introduction to sklearn module of python
- Find for missing value and impute
- Fillna() and Imputer() of sklearn

Visualization library of Python

- Using pandas plotting functions
- Introduction to Matplotlib and Seaborn
- Difference between the two
- Scatter plot using matplotlib
- Draw histogram, barchart, pie chart to any data
- Create pairplot using seaborn
- Concept of Box plot in seaborn
- Drawing of 3D graphs

Introduction to R programming

- Introduction to R and its features
- Install R and RStudio
- Different data types and functions in R
- Control statements and loops in R
- Vectors, lists, matrix, factors in R
- R data frames
- Data reshaping and merging
- Reading data from external file (csv/excel)

R packages for Data manipulation

- What is data exploration and data wrangling
- R packages for data exploration
- Installing and using the packges
- Dplyr package and its functions
- Chaining of different functions
- Data.table package for fastest manipulation in data

Generating plots in R

- Install ggplot2 visualization package
- Concepts of geom() function
- Different charts like, bar plot, histogram, pie chart etc
- Box plot and its uses
- Draw density plot and frequency polygon plot
- Set axis labels and title of R chart
- Draw plot with png in background
- Save plots as an image in disc

Statistical inference

- Why we need statistics
- Terminology used in statistics
- Measure of central tendency
- Measure of spread
- Correlation and covariance
- Standard deviation
- Standardization and normalization
- Normal and binary distribution
- Probability and types of probability
- Hypothesis testing
- ANOVA (Analysis of Variance)

Introduction to Machine learning (using R and Python)

- What is machine learning
- Types of machine learning
- Concepts of supervised and unsupervised learning
- Predictive modeling using Linear Regression
- Simple and multiple linear regression in R and Python
- Assumptions of linear regression
- Accuracy test for the model
- R2 score and RMSE score

Classification Technique

- What is classification and its uses
- Logistic regression to solve classification problem in R and Python
- Difference with linear regression
- Accuracy test for classification
- Confusion matrix and classification report
- True positive, false positive rate
- Decision tree and random forest for classification in R and Python
- Pruning of tress
- Entropy and information gain

Unsupervised learning

- Understanding unsupervised learning
- Clustering and its uses
- Different types of clustering
- K means, canopy clustering and hierarchical clustering
- Mathematics behind K means
- K means process flow
- Implement K means in R and Python

Dimensionality reduction (PCA)

- What is dimensionality reduction
- Feature extraction and feature elimination
- Principal Component Analysis (PCA)
- Apply PCA in R and Python
- Advantages of PCA using example

Projects

- Augmenting retail sales with Data Science
- Prediction on Pokeman dataset
- Analyzing pre-paid model of stock broking
- Email spam filtering
- Bigmart sale analysis
- House price prediction
- Analysis of stock market
- Youtube data analysis

- Gain skills and competencies required in Industry by Experts.
- Work on Real-time Projects depending upon the course you select.
- Students work in a professional corporate environment.
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- Global Brand recognition for Placements.

- Course Duration: 3 MONTHS
- Regular Batches: Online / Offline

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