Data Science Essentials
This is a complete Data Science through a rigorous curriculum developed by the world’s topmost experts in the field, the program covers both the foundations of data sciences, along with applied methods useful in practice. So, Students will learn how to collect, prepare, store, analyze, and visualize data, all at large scales as this course is a blend of data science being applied alongwith the detailed substitutes such as R, Python, Machine Learning, Hadoop, Hive, AI, Deep learning, Tableau, Data extraction, wrangling and many more data analytics tools.
About This Course
In this era of Data trade, with the recent boom in the data industry, data scientist has become a glammed up term familiar for every data oriented Industry. Data Science is a much broader concept where a set of tools and techniques are implied upon to extract the insights from the data. It involves several aspects of mathematics, statistics, scientific methods, Machine Learning etc. to drive the essential analysis of data. Because companies are starting to understand what kind of value Data Scientists can bring to their business, the demand for these profiles is going up. They are automating tasks, delivering deeper insights and uncovering new business opportunities.
Why Data Science?
Traditionally, the data that we had was mostly structured and small in size, which could be analyzed by using simple BI tools. Unlike data in the traditional systems which was mostly structured, today most of the data is unstructured or semi-structured. Data is generated from different sources like financial logs, text files, multimedia forms, sensors, and instruments. Simple BI tools are not capable of processing this huge volume and variety of data. This is why we need more complex and advanced analytical tools and algorithms for processing, analyzing and drawing meaningful insights out of it. This is the reason why Data Science has become so popular.
Who is a Data Scientist?
There are several definitions available on Data Scientists. In simple words, a Data Scientist is one who practices the art of Data Science. The term “Data Scientist” has been coined after considering the fact that a Data Scientist draws a lot of information from the scientific fields and applications whether it is statistics or mathematics.
What does they do?
Data scientists are those who crack complex data problems with their strong expertise in certain scientific disciplines. They work with several elements related to mathematics, statistics, computer science, etc (though they may not be an expert in all these fields). They make a lot of use of the latest technologies in finding solutions and reaching conclusions that are crucial for an organization’s growth and development. Data Scientists present the data in a much more useful form as compared to the raw data available to them from structured as well as unstructured forms.
Business Intelligence vs Data Science
- Business Intelligence (BI) basically analyzes the previous data to find hindsight and insight to describe business trends. Here BI enables you to take data from external and internal sources, prepare it, run queries on it and create dashboards to answer questions like quarterly revenue analysis or business problems. BI can evaluate the impact of certain events in the near future.
- Data Science is a more forward-looking approach, an exploratory way with the focus on analyzing the past or current data and predicting the future outcomes with the aim of making informed decisions. It answers the open-ended questions as to “what” and “how” events occur.
Job Roles
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Statistical Programming Specialist
- Analytics Lead/Manager
- Business Analysts
- Business Intelligence Professional
- Big Data Analyst
- Marketing Analytics Professional
- HR Analytics Professional
Prerequisites
- Basic understanding of mathematics and statistics
- Knowledge of at least one programming language like R or Python
Learning Objectives
Target Audience
- IT Professionals
- Business Analysts
- Analytics Managers
- Supply Chain Network Managers
- Marketing Managers
- Finance and Banking Professionals
Curriculum
Data Gathering
Data Filtering
Data Transformation
Data Exploration
Data Classification & Machine Learning
Data Communication & Visualization
Data Integration
Data Analysis Concepts
