Data Science

Topics covered in this section are:

• Python Introduction
• Indentations in Python
• Python data types and operators
• Python Functions

Learning Outcomes: By the end of this module, you will get the basic Python programming knowledge.

Topics covered in this section are:

• Data Structures Overview
• Identifying the Data Structures
• Allocating values to the Data Structures
• Data Manipulation Significance
• Dplyr Package and performing different data manipulation operations.

Learning Outcomes: Upon completing this module, you will be able to understand the significance of Data structures and Data manipulation in Data science.

Topics covered in this section are:

• Introduction to Data Visualisation
• Various kinds of graphs, Graphics grammar
• Ggplot2 package
• Multivariant analysis by using geom_boxplot
• Univariant analysis by using the histogram, barplot, multivariate distribution, and density plot.
• Creating the bar plots for the categorical variables through geop_bar() and including the themes through the theme() layer.

Learning Outcomes: At the end of this module, you will be able to visualize the data through different graphs, Ggplot2 package. Also, you will get a real-time experience of bar plot creation, Univariant, and Multivariant analysis.

Topics covered in this section are:

• Statistics Importance
• Statistics classification, Statistical terminology.
• Data types, Probability types, measures of speed, and central tendency.
• Covariance and Correlation, Binary and Normal distribution
• Data Sampling, Confidence, and Significance levels.
• Hypothesis Test and Parametric testing

Learning Outcomes: By the end of this module, you will gain practical knowledge of different statistical concepts like Probability types, Hypothesis test, Covariance. You will also be able to work with other statistics techniques like Correlation, Data sampling, Normal and Binary Distribution.

Topics covered in this section are:

• Machine Learning Fundamentals
• Supervised Learning, Classification in Supervised Learning
• Linear Regression and mathematical concepts related to linear regression
• Classification Algorithms, Ensemble Learning techniques

Learning Outcomes: Upon completing this module, you will get a basic knowledge of machine learning, and you will be proficient in Supervised learning, Linear regression, and Ensemble learning.

Topics covered in this section are:

• Logistic Regression Introduction
• Logistic vs Linear Regression, Poisson Regression
• Bivariate Logistic Regression, math related to logistic regression
• Multivariate Logistic Regression, Building Logistic Models
• False and true positive rate, Real-time applications of Logistic Regression

Learning Outcomes: At the end of this module, you will get practical knowledge of Logistic regression, Linear Regression, Poisson Regression, and Logistic models.

This module discusses topics like classification techniques, implementing random forest, Naive Bayes, Entropy, Information Gain, and Gini Index.

Topics covered in this section are:

• Classification Techniques. Decision Tree Induction Algorithm
• Implementation of Random Forest in R
• Differences between classification tree and regression tree
• Naive Bayes, SVM
• Entropy, Gini Index, Information Gain

Learning Outcomes: Upon completing this module, you will acquire an in-depth understanding of decision tree induction algorithms, implementing the random forest in the R programming.

Topics covered in this section are:

• Clustering, K-means clustering, Canopy Clustering, and Hierarchical Clustering
• Unsupervised learning, Clustering algorithm, K-means clustering algorithm
• K-means theoretical concepts, k-means process flow, and K-means implementation.
• Implementing Historical Clustering in R
• PCA(Principal Component Analysis) Implementation in R

Learning Outcomes: Upon completing this module, you will get a real-time experience of k-means clustering, clustering algorithm, and Principal Component Analysis.

Topics covered in this section are:

• Natural language processing and Text mining basics
• Significance and use-cases of text mining
• NPL working with text mining, Language Toolkit(NLTK)
• Text Mining: pre-processing, text-classification and cleaning

Learning Outcomes: At the end of this module, you will get a working knowledge of Natural Language Processing and Text Mining.

Topics covered in this section are:

• Numpy Basics
• Numpy Mathematical Functions
• Probability Basics and Notation
• Correlation and Regression
• Joint Probabilities
• Bayes Theorem
• Conditional Probability, sum rule, and product rule

Learning Outcomes: By the end of this module, you will be able to use probability concepts, Numpy functions, Bayes theorem, Correlation, and Regression in Data Science.

Topics covered in this section are:

• Scipy Introduction and characteristics
• Scipy sub-packages like Integrate, Cluster, Signal, Fftpack, and Bayes Theorem

Learning Outcomes: By the end of this module, you will get a real-time scientific computing experience.

Topics covered in this section are:

• Pyspark basics
• Uses and Need of pyspark
• Pyspark installationAdvantages of pyspark over MapReduce
• Pyspark applications

Learning Outcomes: At the end of this module, you will acquire practical knowledge of Pyspark.

Topics covered in this section are:

• Machine Learning effect on Artificial Intelligence
• Deep Learning Basics, Working of Deep Learning
• Regression and Classification in the Supervised Learning
• Association and Clustering in unsupervised learning
• Basics of Artificial Intelligence and Neural Networks
• Supervised Learning in Neural Networks, multi-layer network
• Deep Neural Networks, Convolutional Neural Networks
• Reinforcement Learning, dnn optimisation algorithms
• Recurrent Neural Networks, Deep learning graphics processing unit
• Deep Learning Applications, Time series modeling

Learning Outcomes: By the end of this module, you will be able to master the deep learning and artificial intelligence concepts required for a data scientist.

Topics covered in this section are:

• Tensorflow Basics and Tensorflow open-source libraries
• Deep Learning Models and Tensor Processing Unit(TPU)
• Graph Visualisation, keras
• Keras neural-network
• Define and Composing multi-complex output models through Keras
• Batch normalization, Functional and Sequential composition
• Implementing Keras with tensorboard, customizing neural network training process
• Implementing neural networks through TensorFlow API

Learning Outcomes: Upon completing this module, you will be able to build deep learning models and visualize the data through Keras and TensorFlow API.

Topics covered in this section are:

• Basics of Autoencoders and rbm
• Implementing RBM for the deep neural networks
• Autoencoders features and applications

Learning Outcomes: At the end of this module, you will achieve hands-on knowledge of Restricted Boltzmann machines and Autoencoders.

Topics covered in this section are:

• Big Data and Hadoop Basics
• Hadoop Architecture, HDFS
• MapReduce Framework and Pig
• Hive and HBase
• Basics of Scala and Functional Programming
• Kafka basics, Kafka Architecture, Kafka cluster and Integrating Kafka with Flume
• Introduction to Spark
• Spark RDD Operations, writing spark programs.
• Spark Transformations, Spark streaming introduction
• Spark streaming Architecture, Spark Streaming Features
• Structured streaming Architecture, Dstreams, and Spark Graphx

Learning Outcomes: By the end of this module, you will acquire real-time experience of working with HDFS, MapReduce framework, HBase, and Kafka. You will also achieve extensive knowledge of developing Spark programs and performing Spark transformations and Spark RDD operations.

Topics covered in this section are:

• Data Visualisation Basics
• Data Visualisation Applications
• Tableau Installation and Interface
• Tableau Data Types, Data Preparation
• Tableau Architecture
• Getting Started with Tableau
• Creating sets, Metadata and Data Blending.
• Arranging visual and data analytics
• Mapping, Expressions, and Calculations
• Parameters and Tableau prep
• Stories, Dashboards, and Filters
• Graphs, charts
• Integrating Tableau with Hadoop and R

Learning Outcomes: By the end of this module, you will get a real-time experience of Creating sets, graphs, charts, dashboards for analyzing data. You will also acquire hands-on knowledge of tableau architecture, tableau installation, tableau prep, and integrating Tableau with R and Hadoop.

This MongoDB module will help you master the concepts like MongoDB basics, MongoDB installation, CRUD operations, Data Indexing, Data Modeling, and Data Administration. Along with this, you will also learn Data Aggregation Schema and Security concepts.

Topics covered in this section are:

• MongoDB and NoSQL Basics
• MongoDB Installation
• Significance of NoSQL
• CRUD Operations
• Data Modeling and Management
• Data Indexing and Administration
• Data Aggregation Schema
• MongoDB Security
• Collaborating with Unstructured Data

Learning Outcomes: At the end of this module, you will get hands-on knowledge of using MongoDB for performing different database operations like creating a database, inserting data into a database, deleting and updating the data. You will also be able to master data modeling, data Indexing, and data administration.

Topics covered in this section are:

• SAS Basics
• SAS Enterprise Guide
• SAS functions and Operators
• SAS Data Sets compilation and creation
• SAS Procedures
• SAS Graphs
• SAS Macros
• PROC SQL
• Advance SAS

Learning Outcomes: By the end of this module, you will be able to carry out advanced data analysis by using SAS concepts.

Topics covered in this section are:

• Entering Data
• Logical Functions
• Conditional Formatting
• Validation, Excel formulas
• Data sorting, Data Filtering, Pivot Tables
• Creating charts, Charting techniques
• File and Data security in excel
• VBA macros, VBA IF condition, and VBA loops
• VBA IF condition, For loop
• VBA Debugging and Messaging

Learning Outcomes: At the end of this module, you will acquire a working knowledge of excel and VBA.