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Post Graduate Program in Data Science and Engineering
Join the Post Graduate Program in Data Science and Engineering by Great Learning designed exclusively for freshers and early career professionals.
Full time
5 Months
₹ 350,000
Quick facts
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Medium of instructions
English
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Mode of Delivery
Video and Text Based
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Frequency of Classes
Weekdays
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Course overview
PG Program in Data Science and Engineering by Great Learning is a 5-month program, mainly designed for those candidates who have a keen interest in programming. The young professionals who want to explore rewarding careers in Data Science and analytics can pursue this course. With a large number of job openings in data science and engineering, this certification course equips you with the adequate skills and knowledge required to stand among the crowd.
The certification program is well-designed to get the learners employed by offering a broad understanding of advanced concepts of data science. This PG Program in Data Science and Engineering certification course will enable fresh graduates to implement Big Data techniques by using SQL, MySQL, Excel, Storm, and other programming languages. After completing the PG Program in Data Science and Engineering course, candidates will be considered as competent plus strong data scientists. Many institutes assign labs and project work to students to test what students have learned.
The highlights
- The course duration is 5 months
- Certificate from Great Lakes Learning
- Comprehensive Curriculum
- Immersive lectures by renowned professors
- In-class data science lab sessions
- Dedicated placement assistance
- Classroom learning format
Program offerings
- Projects
- Workshops
- Classroom sessions
- Lab work
- Placement opportunities
Course and certificate fees
Fees information
Generally, the fee for pursuing a Post Graduate Program for Data Science and Engineering is basically defined and there will be extra charges for GST. On average, the fee for the Post Graduate Program in Data Science and Engineering is Rs. 3,50,000 along with the GST. This fee includes all the applicable program charges.
certificate availability
certificate providing authority
Eligibility criteria
For class 10th and class 12th
A minimum of 60% in class 10th as well as 12th from any recognised board
Graduation Degree
Graduation degree in engineering, computer science, statistics, IT, mathematics, actuarial science, economics, electronics, life science with a minimum of 60% marks
Working Experience
The candidates in the final semester can also apply or fresh graduates with zero to three years of work experience
Moreover, it’s not necessary that all candidates applying for the PG Program in Data Science and Engineering certification course by Great Learning must have prior knowledge of data science. Anyone passionate about mastering data science and meeting all the eligibility requirements can apply for this course.
What you will learn
The candidates get a chance to study in a collaborative environment from top industry leaders and renowned faculty. The PG Program in Data Science and Engineering certification program comprises practical sessions, live lectures, and some lab work. Thus, the candidates are capable of:
- Understand Big Data technologies: To deploy enterprise information management as well as solve various business problems, it’s necessary to understand Big Data technologies. With the increasing time and advanced technologies, companies demand professionals who are well familiar with the use of technology. PG programs in data science and engineering help students to acknowledge the basics of the operating system, stacks, ques, and other information regarding data science.
- Learn Artificial Intelligence: Many students think that machine learning and artificial learning are the same concepts. However, the course also helps students to understand the basic difference between two terms and their specific uses. The course enables the learners to go through with the concept of a neural network that highlights the creation of intelligent machines.
- Get an overview of machine learning: If candidates want to become great at machine learning, they need to become capable of answering questions related to it, which requires both deep understanding and experience. By learning and practising, the candidates become capable of creating their data science projects.
- Solve analytical problems: The program helps students to communicate analytical problems and various methods visually, orally, and in written format. The graduates become capable of assisting multinational companies to make critical decisions through modelling, analysis, and visualisation.
- Experience in software tools: The students get a chance to learn the most advanced concepts plus techniques in data science. They get hands-on experience in software tools such as SQL, Tableau, R, Python, and many more. It’s a great chance for the learners to become a master in handling data plus present multiple analysis models after completing this course. They can learn various models, business decision-making skills, and algorithms of forecasting.
Who it is for
- Fresh graduates who have a keen interest in machine learning, data science, and artificial intelligence
- Managers, working professionals, or any other individual who would need to extract information from data that they usually come across from different areas. It can be banking, retail, BPO, Supply Chain, strategic management, and so on.
- Marketing and sales professionals who want to enhance their resume
- Software programmers with a minimum of 2 years of experience in using quantitative techniques as well as data analytical tools. Additionally, anyone interested in learning the use of data science for effective decision-making will find this program challenging and stimulating.
- Professionals interested in learning the multiple techniques of data science and the implementation can also enrol for the course.
- Individuals who have a high interest in data structure and programming.
Admission details
- Fill the application form: All the interested aspirants are required to apply for the Post Graduate Program in Data Science and Engineering by filling the online application form.
- Admission test and interview: An admission panel will shortlist the applicants based on the application form submitted by them. The candidates have to go through an admission test plus a screening call with the Admission Director’s office.
- Program Joining: An offer to admission to the selected aspirants will be made. The candidates will accept the proposal by paying the program fee.
Filling the form
In the application form for Post Graduate Program in Data Science and Engineering, the candidates have to fill in their basic information that includes:
- Name
- Mobile number
- Email address
- Current city
- Preferred campus
After that, candidates have to fill in their professional details:
Undergraduate details
- Degree: The learners have to enter their graduate degrees in this section.
- Specialisation: It can be computer science, mathematics, statistics, or any other subject.
- College/ University: The students have to enter the name of their college or university from which they have completed their graduation degree.
- Graduation Year: The undergraduate degree passing year.
- CGPA/ Percentage: The candidates have to enter their overall percentage obtained after completing the graduation degree.
Post Graduation (if any)
The learners who have completed their post-graduation degree and are now applying for a Post Graduate Program in Data Science and Engineering should mention the details. Apart from it, the learners can mention the score for CAT, GMAT, or GRE (optional)
The syllabus
Foundations
Introduction to programming using Python
Exploratory Data Analysis
- Pandas
- Summary statistics (mean, median, mode, variance, standard deviation)
- Seaborne
- matplotlib
Statistical Methods for Decision Making
- Probability distribution
- Normal distribution
- Poisson's distribution
- Bayes’ theorem
- Central limit theorem
- Hypothesis testing
- One-Sample T-Test
- ANOVA and Chi-Square
SQL Programming
- Introduction to DBMS
- ER diagram
- Schema design
- Key constraints and basics of normalization
- Joins
- Subqueries involving joins and aggregations
- Sorting
- Independent subqueries
- Correlated subqueries
- Analytic functions
- Set operations
- Grouping and filtering
Machine Learning Techniques
Linear and Logistic Regression
- Multiple linear regression
- Fitted regression lines
- AIC, BIC, Model Fitting, Training and Test Data
- Introduction to Logistic regression, interpretation, odds ratio
- Misclassification, Probability, AUC, R-Square
Supervised Learning Classification
- CART
- KNN (classifier, distance metrics, KNN regression)
- Decision Trees (hyper parameter, depth, number of leaves)
- Naive Bayes
Unsupervised Learning
- Clustering - K-Means & Hierarchical
- Distance methods - Euclidean, Manhattan, Cosine, Mahalanobis
- Features of a Cluster - Labels, Centroids, Inertia
- Eigenvectors and Eigenvalues
- Principal component analysis
Ensemble Techniques
- Bagging & Boosting
- Random Forest
- AdaBoost & Gradient boosting
- Hackathon
Applications
Time Series
- Trend and seasonality
- Decomposition
- Smoothing (moving average)
- SES, Holt & Holt-Winter Model
- AR, Lag Series, ACF, PACF
- ADF, Random walk and Auto Arima
Text Mining
- Text cleaning, regular expressions, Stemming, Lemmatization
- Word cloud, Principal Component Analysis, Bigrams & Trigrams
- Web scrapping, Text summarization, Lex Rank algorithm
- Latent Dirichlet Allocation (LDA) Technique
- Word2vec Architecture (Skip Grams vs CBOW)
- Text classification, Document vectors, Text classification using Doc2vec
Data Visualization
- Building interactive dashboards using Tableau
- Data Visualization using Tableau
Capstone Project
Career Preparation
- Aptitude Skill Training and Development
Scholarship Details
Not all institutions offer scholarships to candidates. Those online institutions which offer often award according to the strength of the candidate’s profile plus their interview process.
Evaluation process
Various institutions providing Post Graduate Program in Data Science and Engineering certificates online try their best to ensure that candidates receive the best possible learning experience in data science. That is why they want to make sure that all the participants of the program show a high level of commitment and passion for the course.
The applicants have to pass a selection test conducted by interviewers. The test checks the aptitude plus the quantitative abilities of the candidate. Data science is undoubtedly a renowned field, but at the same time, can be challenging when it comes to clear the admission test.
How it helps
- Enhance knowledge and skills: After achieving the Post Graduate Program in Data Science and Engineering certification, the candidates can gain efficient knowledge and skills. Generally, it’s a 30 to a 31-credit interdisciplinary program that involves computer science, mathematics, data management, machine learning, applied statistics, and computation.
- Wide-ranging job options: Candidates holding a certificate in the Post Graduate Program in Data Science and Engineering can find a good job. They can seek their future as Business Intelligence Reporting Professional, Statistician, Data Analyst, Big Data Engineer, Data Mining, Project Manager, or any other job role.
- Potential Career Growth: Not only for IT Managers but also for other business leaders looking to break into their career path, a robust foundation of skills-based training can be helpful. The candidates get a tremendous knowledge of key software tools.
- High salary expectations: Generally, companies hiring professionals with machine learning skills are paying an average of Rs. 9.02 lakhs (approx.) for one year. Though the salary range varies according to the job profile as well as working experience; still the candidates can demand a salary package above Rs. 7.5 lakhs (approx.) for a year.
- Practice Python and R: Python and R are the main programming languages for learning data science. Both languages have a wealth of packages and are highly popular in the industry. The course helps candidates to practice these two programming languages as much as you can.
- Learn data analysis and visualisation with Pandas: If learners want to work with data in Python, then they must acknowledge how to use the Pandas library. Learning Pandas will significantly enhance your efficiency while working with data as it helps in handling messy data, visualising data, filtering data, and so much more.
Instructors
Dr Abhinanda Sarkar
Academic Director
Great Learning
Other Bachelors, Other Masters, Ph.D
Dr D Narayana
Professor
Great Learning
Ph.D
Dr Mudit Kulshreshtha
Professor
Great Lakes Gurgaon
Other Masters, Ph.D
Dr Srabashi Basu
Professor
Great Learning
Ph.D
Mr Rajesh Jakhotia
Instructor
IIM Calcutta
Mr Mukesh Rao
Professor
Great Learning
Mr Gurumoorthy Pattabiraman
Faculty
Great Learning
Other Masters
Mr Deepesh Singh
Executive Programmer
IIM Lucknow
Mr Kathirmani Sukumar
Instructor
Freelancer
B.E /B.Tech
Mr Saurabh Agarwal
Professor
HBTU Kanpur
M.E /M.Tech.
Mr Pushkar Shah
Faculty
Great Learning
M.S
Mr R Vivekanand
Operations Director
Freelancer
MBA
FAQs
Business Intelligence- Advanced SQL, NoSQL Databases, Storytelling with Advanced Visualization, Natural Language Processing- Advanced Machine Learning, Business Analytics- Storytelling and Advanced Business Problem Solving, Advanced Machine Learning, Data Engineering- Data Warehousing, Data Modeling, Data Streaming, Building Data Pipelines, and Processing.
If you enjoy studying data science concepts, machine learning, computer networks, and using them to inform business decisions, then this program is for you.
Functional Analytics, Statistical Analysis, Hypothesis Testing, Text Mining, Regression Modeling, Neural Networks, Deep Learning, Predictive Analytics, Machine learning using R, Python, and Spark.
Data Analyst, Business Analyst, Data Engineer, Machine Learning Engineer in IT Companies, and Data Scientist. Learners can apply for a post in MNC Financial Institutions, In-House Analytics Units of Corporations, Healthcare, Oil & Gas Industry, Manufacturing Companies, Niche Analytics Firms.
Generally, it depends on the institution, whether they offer any preparation material or not. There are various institutions that provide a preparatory course once students make their first instalment.
To prepare for the data science exam, candidates must possess knowledge of a programming language plus the capability of the candidate to work with data in that programming language.
Pursuing Post Graduate Program in Data Science and Engineering is easy for those learners who have a keen interest in machine learning, cognitive tools, deep learning, and other data science concepts.
The interviewers generally ask questions and expect that the applicant answers them using data. It means that learners have to gather relevant data, explore, analyse, and then visualise the collected data.
Data science is a must for multinational companies. While there is a rising demand for data engineers, it makes a sense to seek a career in data science and engineers, and thus the course is beneficial for you.
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