Data Scientist is amongst the trendiest jobs, Glassdoor ranked it as the #1 Best Job in America in 2018 for the third year in a row, and it still holds its #1 Best Job position. Python is now the top programming language used in Data Science, with Python and R at 2nd place.

Data Science is a field where data is analyzed with an aim to generate meaningful information. Today, successful data professionals understand that they require much-advanced skills for analyzing large amounts of data. Rather than relying on traditional techniques for data analysis, data mining and programming skills, as well as various tools and algorithms, are used. While there are many languages that can perform this job, Python has become the most preferred among Data Scientists.

Today, the popularity of Python for Data Science is at its peak. Researchers and developers are using it for all sorts of functionality, from cleaning data and Training models to developing advanced AI and Machine Learning software. As per Statista, Python is LinkedIn's most wanted Data Science skill in the United States.

Data Science with R, Python and Spark Training lets you gain expertise in Machine Learning Algorithms like K-Means

Clustering, Decision Trees, Random Forest, and Naive Bayes using R, Python and Spark. Data Science Training

encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction

to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on

Media, Healthcare, Social Media, Aviation and HR.

Curriculum

Introduction to Data Science

Learning Objectives - Get an introduction to Data Science in this module and see how Data Science

helps to analyze large and unstructured data with different tools.

Topics:

What is Data Science? What does Data Science involve?

Era of Data Science Business Intelligence vs Data Science

Life cycle of Data Science Tools of Data Science

Introduction to Big Data and Hadoop Introduction to R

Introduction to Spark Introduction to Machine Learning

Statistical Inference

Learning Objectives - In this module, you will learn about different statistical techniques and

terminologies used in data analysis.

Topics:

What is Statistical Inference? Terminologies of Statistics

Measures of Centers Measures of Spread

Probability Normal Distribution

Binary Distribution

Data Extraction, Wrangling and Exploration

Learning Objectives - Discuss the different sources available to extract data, arrange the data in

structured form, analyze the data, and represent the data in a graphical format.

Topics:

Data Analysis Pipeline What is Data Extraction

Types of Data Raw and Processed Data

Data Wrangling Exploratory Data Analysis

Visualization of Data

Introduction to Machine Learning

Learning Objectives - Get an introduction to Machine Learning as part of this module. You will

discuss the various categories of Machine Learning and implement Supervised Learning Algorithms.

Topics:

What is Machine Learning? Machine Learning Use-Cases

Machine Learning Process Flow Machine Learning Categories

Supervised Learning algorithm: Linear

Regression and Logistic Regression

• Define Data Science and its various stages

• Implement Data Science development methodology in business scenarios

• Identify areas of applications of Data Science.

• Understand the fundamental concepts of Python

• Use various Data Structures of Python.

• Perform operations on arrays using NumPy library

• Perform data manipulation using the Pandas library

• Visualize data and obtain insights from data using the Matplotlib and Seaborn library

• Apply Scrapy and Beautiful Soup to scrap data from websites

• Perform end to end Case study on data extraction, manipulation, visualization and analysis using Python