Course syllabus - AptusLearn
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Course syllabus

Where Learning Begins..

(Blog)probability-and-statistics

Probability And Statistics

Introduction to probability and statistics with applications. Topics include: basic combinatorics, random variables, probability distributions, Bayesian inference, hypothesis testing, confidence intervals, and linear regression.

(Blog)database

Databases

Foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions.

(Blog)machine-learning

Machine Learning & Artifical Intelligence

Overview of concepts, techniques, and algorithms in machine learning and topics such as classification and linear regression 
(Blog)deep learning

Deep Learning

Deep learning, a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web
(Blog)Data mining

Data Mining

Methods and Algorithms for recognizing patterns and making predictions data mining and predictive analysis 

(Blog)business analytics

Business Analytics

Exercises on business case studies for relevant theory  using various tools and languages learnt in other practical courses.
(Blog)bigdata hadoop

Big Data & Hadoop

Big Data engineering with Hadoop ecosystem components such as HDFS, Map-Reduce, YARN, HiveQL, HBase and PIG. 

(Blog)database visualisation

BI & Data Visualization

Business intelligence, Data warehousing, and Data visualization concepts and approaches for analyzing and presenting data views. 

CUrriculum

Course Details

CodeCourseTheory/PracticalCreditsHours
DS01Foundations of Data ScienceBoth020

 

CodeCourseTheory/PracticalCreditsHours
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
TOTAL26260

 

CodeCourseTheory/PracticalCreditsHours
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
TOTAL25250
Student may opt for one elective from DS21 & DS22 OR DS23 & DS24

 

CodeCourseTheory/PracticalCreditsMonths
DS25Industry internshipProject93

 

COURSE TOPICS

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.

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