PG Diploma In Data Science - AptusLearn
Bhubaneswar : +91 96069 81950 ,   +91 96069 81951

PG Diploma in Data Science

In association with IIIT Bhubaneswar

Data science is revolutionizing the world around us. To dive into the waters of this niche industry and swim to the shore of the success, one needs to have hands-on experience in bleeding edge technologies like data mining, business intelligence, analytics, and applications expertise combined with knowledge in tools, such as R, Python, TensorFlow, RapidMiner, PowerBI, Tableau, Hadoop, and Spark.

To cater to this need, AptusLearn  is offering 1 year PG Diploma in Data Science (PGDDS) in collaboration with IIIT, Bhubaneswar. Empowered with great faculty and infrastructure support from such a prestigious institution, this premium course provides interactive teaching and live mentoring. The course gives great emphasis on holistic understanding of concepts and hands-on learning — all these complemented with great study materials.

Ideal for working professionals and students keen to find their love for data, the academia encourages candidates to move at the speed of technology, innovation and efficiency revolving around Data Science and Data Analytics.


This course illustrates how to apply statistics, mathematics, databases, machine learning, data mining, business intelligence and analytics, and big data engineering for decision making under uncertainty today. The participants will acquire conceptual and applications expertise required for a Data Scientist, through use of latest tools and techniques in simple use cases.

Batch Details

  • Full-time batches for Graduates (Students graduating in 2019 can also apply)


  • Basic knowledge of computer programs, probability and statistics
  • Laptop, of approved configuration, for hands-on (workshop) practice




IIIT, Bhubaneswar campus


July 2019 to March 2020, on weekdays (Monday to Friday)




Expert in Data and Decision sciences area


Statistics, Mathematics, Databases, Machine Learning, Data mining, Business Intelligence and Analytics, Big data


SQL, R, Python, TensorFlow, RapidMiner Studio, MS Excel and PowerBI, Tableau, Hadoop and Spark


Direct and virtual, Theory (~40%) and Workshop (~60%)


CGPA based on Objective tests, Assignments and Project work (Business use cases or field work)


IIIT Bhubaneswar awards PG Diploma on successful completion


Graduates or Students in final year of graduation


Data Scientist in IT or Consulting firms


Eligible to apply for Campus placement by Aptus Data Labs, other IT and consulting firms. Free placement guidance and assistance by a dedicated desk for one year.


INR 4.5 lakhs +GST (@18% as applicable), payable in 3 instalments: 40% before course starts, 30% before 2nd trimester and 30% before 3rd trimester. Early bird or referral and loyalty discounts are as applicable. Financial Aid available as per the norms of the bank.


College canteen food and bus transport are available. Hostel stay is optional. These facilities are payable and subject to availability.


Course Details

DS01Foundations of Data ScienceBoth020


DS02Data Science OverviewTheory220
DS03Foundations of Probability and StatisticsTheory330
DS04Probability and Statistics with RPractical330
DS05Advanced decision modelsTheory220
DS06Decision models using MS ExcelPractical220
DS07Introduction to Database systemsTheory220
DS08Structured Query LanguagePractical220
DS09Machine Learning -ITheory220
DS10Python for Data AnalyticsPractical330
DS11Business analytics for decisionsTheory220
DS12Business analytics for decisions LabPractical330


DS13Machine Learning -IITheory220
DS14Machine Learning with TensorFlowTheory330
DS15Data Mining and Predictive AnalyticsTheory220
DS16Data analytics with RapidMiner StudioPractical330
DS17Business Intelligence & Data VisualizationTheory220
DS18Data visualisation with PowerBI & TableauPractical330
DS19Big Data engineeringTheory220
DS20Big Data and Hadoop ecosystemPractical330
DS21Social network and Web AnalyticsTheory220
DS22Streaming Analytics with Hadoop & SparkPractical330
DS23Financial market risks analysisTheory220
DS24Financial market risks analysis LabPractical330
Student may opt for one elective from DS21 & DS22 OR DS23 & DS24


DS25Industry internshipProject93



The course builds foundation concepts and exposure to data science and analytics techniques, before entering the program. This is recommended for students with limited prior knowledge on statistics, databases, computing algorithms and languages.


The course provides overview of data science, associated concepts, tools and applications for decision making under uncertainty.

The course builds foundations on probability, descriptive and inferential statistics, exploratory data analysis, descriptive and predictive analysis required for advanced data analysis.

The course involves hand-on practice for relevant theory (DS03) with “R” packages.

The course explains use of advanced optimization, stochastic and mathematical models to meet multiple objectives under uncertainty.

The course illustrates use of advanced analytics tools in decision making under uncertainty, using business case studies.

The course covers Business intelligence, Data warehousing, and Data visualisation concepts and approaches for analysing and presenting data views.

The course builds theoretical foundations on big data engineering with Hadoop ecosystem components such as HDFS, Map-Reduce, YARN, HiveQL, HBase and PIG.

The course covers practical approaches to big data engineering with Hadoop ecosystem components such as HDFS, Map-Reduce, YARN, HiveQL, HBase and PIG.

The course builds understanding and approaches to web intelligence and social network analytics.

The course involves hand-on practice for relevant theory (DS05) with MS-Excel add-ins.

The course provides hands-on experience on data visualisation with PowerBI and Tableau.

The course explains basics of data models, database systems and query language.

The course involves hand-on practice for relevant theory (DS07) with SQL (MySQL or Microsoft SQL Server).

The course covers support vector machines, neural networks, graphical models and pattern recognition that can learn from data.

The course covers unsupervised and evolutionary learning algorithms, and advanced neural networks that enable advanced machine learning from data.

The course involves hand-on practice for relevant theory (DS09) with Python language and tool/ packages.

The course explains concepts, methods and algorithms for data mining and predictive analytics.

The course focuses on data mining and predictive analytics with RapidMiner Studio.

The course illustrates use of volatility risk analysis models in financial markets through case studies and MS Excel add-ins.

The course covers practical approaches to streaming analytics with Hadoop ecosystem components such as HDFS, Map-Reduce, Spark, Kafka, Flume and NoSQL.

The course involves students in a project team where they solve practical problems in an institutional environment such as industry, consulting or R&D unit. They may use relevant tools and languages learnt during the academic program or available at the institution.

The course involves hand-on practice for relevant theory (DS13) on machine learning, with Google TensorFlow framework.

The course involves practice exercises on business case studies for relevant theory (DS23), using various tools and languages learnt in other practical courses.

Join us today, for a better future !!!