DATA SCIENCE COURSE

Data Science

Data Science is a field in the interdisciplinary area or domain. The phrase “Data Science” was first invented by William S. Cleveland in the beginning of the 21^st Century. It is about the logical methodology in handling huge data requirements. The study of information relates to different scientific procedures, theories and techniques derived from several fields in the framework of mathematics, insights/statistics, computer science, domain knowledge and information science.

Data science is an interdisciplinary field that utilizes logical techniques, processes, calculations and systems to separate information and experiences from organized and unstructured information, and apply knowledge and actionable insights of knowledge from information across a wide scope of application spaces.

Recently, Data Science has been in huge demand in any organization or business. To adapt to the demand, aspirants have begun in looking forward their career in Data Science courses.

Organizations have begun in upgrading the skills of their staff to remain aggressive towards their work. So many institutes picked the business needs and planned reasonable courses in Data Science.

Data Science Eligibility

In Data Science most of courses planned are PG and certificate level courses for graduates. The eligibility criterion for the PG data science diploma program is given below:-

Aspirant who has done graduation can apply for this course
If you have a works experience in the field of data science along with your bachelor’s degree are also eligible to apply for this course
IT engineers who wants to shift their career into data science are also eligible

Latest Admission Alert

IMS GHAZIABAD

Institute of Management Studies (IMS), Ghaziabad is one of the Top 10 B-Schools in North India. Application invited for PGDM | Ph.D.

IBMR BUSINESS SCHOOL, GURUGRAM

Applications Open | MBA | PGDM

Data Science Course Syllabus

Data science is an electric field. It will provide work to numerous experts later on in the future. Thinking about its significance and vastness, numerous fields are included in its program.

Given below the syllabus of the Data science program:-

Introduction to Data Science	Statistical Foundations for Data Science
Mathematics and Statistics	Coding
Domain Expertise	Machine Learning Algorithms
Data Analysis	Data Wrangling
Data Visualization	Data Structures and Algorithms
Scientific Computing	Optimization Techniques
Experimentation, Evaluation and Project Deployment Tools	Predictive Analytics and Segmentation using Clustering
Exploratory Data Analysis	Business Acumen & Artificial Intelligence
Matrix Computations	Scholastic Models

Subjects and Skills Required for Data Science

In general, for getting admission in Data Science program, the following qualifications are required: –

A bachelor’s degree from the STEM (Science, technology, engineering, and mathematics) stream.
There no requirement for any experience is coding.
Mathematics is the main subject of Data Science program and Data Analysis, where the model is created by processing mathematical algorithms’ data.
Statistics will help the aspirants to analyze, understand and derive the conclusion from the data.
Explore Exploratory Data Analysis and Excel to obtain useful insights from the collection of data/information and learn from its attributes.
A good understanding of the databases is highly attractive skill for the organization.

Data Science Study Material and Books

The top data science courses books and study material recommended by top data science professionals are mentioned below:

Book	Author
Think Bayes – Bayesian Statistics Made Simple	Allen B Downey
Python Data Science Handbook	Jake VanderPlas
Think Python	Allen B Downey
Understanding Machine Learning- From Theory to Algorithms	Shai Shalev-Shwartz and Shai Ben-David
Mining of Massive Datasets	Jure Leskovec, Anand Rajaraman, Jeff Ullman
Introduction to Statistical Learning	Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
The Elements of Statistical Learning	Trevor Hastie, Robert Tibshirani, Jerome Friedman
Deep Learning	Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Bayesian Methods for Hackers	Cam Davidson – Pilon
Think Stats	Allen B Downey
R for Data Science	Garret Grolemund and Hadley Wickham
Advanced R	Hadley Wickham