Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems.

It is ultimately about using data in creative ways to generate business value

A Data Scientist does the exploratory analysis to discover insights from the data

So, Data Science is primarily used to make decisions and predictions

**Data Science – The Future**

28% Rise in demand for Data Scientists by 2020

3 out of 5 Highest paying professionals are related to Data Science and AI

36% Higher base salary is what the Data scientists earn compared to other analytics professionals.

**IT Industry A Changing Scenario**

The global IT services market is expected to grow by over $1160 Billion in 2020

Worldwide IT spending is forecast to reach $2.7 Trillion in 2020

This shows the immense potential in the IT industry for a lucrative career

The IT industry is experiencing a revolution with streams like Data Science, Machine Learning, Artificial Intelligence, and Software Testing coming up rapidly

These are the jobs of the future and will continue to be in great demand

**Software Trained:** MS Office, Python, MongoDB, R Studio

**Job Profile**: Entry Level Data Analyst

**Learning Objectives:**

- Work with spreadsheets and analyze data using MS Excel
- Learn the basics of social media, mobile technology, analytics, and cloud computing along with an understanding of their interconnectivity
- Learn the basic concepts of object-oriented programming
- Learn how to program with the popular development language, Python
- Build Web applications using Python
- Learn MongoDB concepts, features, architecture, and data model, and how to install, configure and monitor open-source databases
- Master data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language
- Develop a real-world application using the R language

** Study Materials**: Students will get a printable softcopy through Onlivarsity.com.

**Entry Requirements**: Since these are professional qualifications in programming, a person should have knowledge of basic computer applications and an understanding of programming languages like C, C++, and Java.

**Award of Certifications**: After the completion/passing of each exam module, the candidate will get certification from APTECH Computer Education, India.

**Step 1: Programming principles and techniques & SMAC** **Step 2: Introduction to Data Science & Basic Statistics****Step 3: Python for Data Science**

Key Learning Objectives:

Write your first Python program by implementing concepts of variables, strings, functions, loops, and conditions. Understand the nuances of lists, sets, dictionaries, conditions and branching, and objects and classes. Work with data in Python such as reading and writing files, loading, working, and saving data with Pandas.

Lesson 01 – Python Basics

Lesson 02 – Python Data Structures

Lesson 03 – Python Programming Fundamentals

Lesson 04 – Working with Data in Python

Lesson 05 – Working with NumPy Arrays

**Step 4: Data Science with R**

Key Learning Objectives:

Gain a foundational understanding of business analytics Install R, R-studio, and workspace setup, and learn about the various R packages Master R programming and understand how various statements are executed in R Gain an in-depth understanding of data structure used in R and learn to import/export data in R Define, understand and use the various apply functions and DPYR functions Understand and use the various graphics in R for data visualization Gain a basic understanding of various statistical concepts Understand and use hypothesis testing method to drive business decisions Understand and use linear, non-linear regression models, and classification techniques for data analysis Learn and use the various association rules and Apriori algorithm Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering.

Lesson 01 – Introduction to Business Analytics

Lesson 02 – Introduction to R Programming

Lesson 03 – Data Structures

Lesson 04 – Data Visualization

Lesson 05 – Statistics for Data Science I

Lesson 06 – Statistics for Data Science II

Lesson 07 – Regression Analysis

Lesson 08 – Classification

Lesson 09 – Clustering

Lesson 10 – Association

**Step 5: Data Science with Python**

Key Learning Objectives:

Gain an in-depth understanding of Data Science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics Install the required Python environment and other auxiliary tools and libraries. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions. Perform high-level mathematical computing using the NumPy package and its vast library of mathematical functions. Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave Perform data analysis and manipulation using data structures and tools provided in the Pandas package Gain expertise in Machine Learning using the Scikit-Learn package Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline.

Lesson 01 – Data Science Overview

Lesson 02: Data Analytics Overview

Lesson 03: Statistical Analysis and Business Applications

Lesson 04: Python Environment Setup and Essentials

Lesson 05: Mathematical Computing with Python (NumPy)

Lesson 06 – Scientific computing with Python (Scipy)

Lesson 07 – Data Manipulation with Pandas

Lesson 08 – Machine Learning with Scikit–Learn

Lesson 09 – Natural Language Processing with Scikit Learn

Lesson 10 – Data Visualization in Python using matplotlib This lesson teaches you to visualize data in python using matplotlib and plot them.

Lesson 11 – Web Scraping with BeautifulSoup

Lesson 12 – Python integration with Hadoop MapReduce and Spark

**Step 6: SQL (MySQL) and NoSQL (MongoDB)**

Key Learning Objectives:

Understand databases and relationships Use common query tools and work with SQL commands Understand transactions, creating tables and views Comprehend and execute stored procedures.

Lesson 1- Fundamental SQL Statements

Lesson 2-Restore and Back-up

Lesson 3-Selection Commands: Filtering

Lesson 4-Selection Commands: Ordering

Lesson 5-Alias

Lesson 6-Aggregate Commands

Lesson 7-Group By Commands

Lesson 8-Conditional Statement

Lesson 9-Joins

Lesson 10-Subqueries

Lesson 11-Views and Index

Lesson 12-String Functions

Lesson 13-Mathematical Functions

Lesson 14-Date – Time Functions

Lesson 15-Pattern (String) Matching

Lesson 16-User Access Control Functions

**Step 7: Machine Learning**

Key Learning Objectives:

Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modeling Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach that includes working on four major end-to-end projects and 25+ hands-on exercises Acquire thorough knowledge of the statistical and heuristic aspects of Machine Learning Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python Validate Machine Learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithms, which include Boosting & Bagging techniques Comprehend the theoretical concepts and how they relate to the practical aspects of Machine Learning.

Lesson 01 – Introduction to Artificial Intelligence and Machine Learning

Lesson 02: Data Wrangling and Manipulation

Lesson 03: Supervised Learning

Lesson 04: Feature Engineering

Lesson 05: Supervised Learning-Classification

Lesson 06: Unsupervised learning

Lesson 07: Time Series Modelling

Lesson 08: Ensemble Learning

Lesson 09: Recommender Systems

Lesson 10: Text Mining

**Step 8: Tableau**

Key Learning Objectives:

Become an expert on visualization techniques such as heat map, treemap, waterfall, Pareto Understand metadata and its usage Work with Filter, Parameters, and Sets Master special field types and Tableau-generated fields and the process of creating and using parameters Learn how to build charts, interactive dashboards, story interfaces, and how to share your work Master the concepts of data blending, create data extracts and organize and format data Master arithmetic, logical, table, and LOD calculations

Lesson 01 – Getting Started with Tableau

Lesson 02 – Core Tableau in Topics

Lesson 03 – Creating Charts in Tableau

Lesson 04 – Working with Metadata

Lesson 05 – Filters in Tableau

Lesson 06 – Applying Analytics to the worksheet

Lesson 07 – Dashboard in Tableau

Lesson 08 – Modifications to Data ConnectionsLesson 09 – Introduction to Level of Details in Tableau (LODS)

**Step 9: Project**

Key Learning Objectives:

Data Processing – In this step, you will apply various data processing techniques to make raw data meaningful. Model Building – You will leverage techniques such as regression and decision trees to build Machine Learning models that enable accurate and intelligent predictions. You may explore Python, R to build your model. You will follow the complete model-building exercise from data split to test and training and validating data using the k-fold crossvalidation process. Model Fine-tuning – You will apply various techniques to improve the accuracy of your model and select the champion model that provides the best accuracy. Dashboarding and Representing Results – As the last step, you will be required to export your results into a dashboard with meaningful insights using Tableau.