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.
- 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
PG DIPLOMA IN DATA SCIENCE - FULL TIME PROGRAM
IIIT, Bhubaneswar campus
July 2019 to March 2020, on weekdays (Monday to Friday)
NUMBER OF SEATS
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
WHO CAN APPLY
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.
Code Course Theory/Practical Credits Hours DS01 Foundations of Data Science Both 0 20 Code Course Theory/Practical Credits Hours DS02 Data Science Overview Theory 2 20 DS03 Foundations of Probability and Statistics Theory 3 30 DS04 Probability and Statistics with R Practical 3 30 DS05 Advanced decision models Theory 2 20 DS06 Decision models using MS Excel Practical 2 20 DS07 Introduction to Database systems Theory 2 20 DS08 Structured Query Language Practical 2 20 DS09 Machine Learning -I Theory 2 20 DS10 Python for Data Analytics Practical 3 30 DS11 Business analytics for decisions Theory 2 20 DS12 Business analytics for decisions Lab Practical 3 30 TOTAL 26 260
|DS13||Machine Learning -II||Theory||2||20|
|DS14||Machine Learning with TensorFlow||Theory||3||30|
|DS15||Data Mining and Predictive Analytics||Theory||2||20|
|DS16||Data analytics with RapidMiner Studio||Practical||3||30|
|DS17||Business Intelligence & Data Visualization||Theory||2||20|
|DS18||Data visualisation with PowerBI & Tableau||Practical||3||30|
|DS19||Big Data engineering||Theory||2||20|
|DS20||Big Data and Hadoop ecosystem||Practical||3||30|
|DS21||Social network and Web Analytics||Theory||2||20|
|DS22||Streaming Analytics with Hadoop & Spark||Practical||3||30|
|DS23||Financial market risks analysis||Theory||2||20|
|DS24||Financial market risks analysis Lab||Practical||3||30|
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.