Data Analytics


Being a data scientist, one can do data analysis in a business, to evaluate what users want, and how the products or services be such crafted to solve customer problems. The inferences can be drawn using complex procedures and algorithms, supported by the analysis done with the data.

Course Goals

  • Have a detailed understanding of crucial technologies and tools in data science and analytics such as data mining, artificial intelligence based machine learning, methods of data visualisation, statistics and probability, and predictive modelling.
  • Develop decision making ability in different use case scenarios.
  • Carry a functional hands-on knowledge related to big data and statistical programming tools.
  • Capable of applying data evaluation approaches and quantitative modelling to have interactive findings, and achieving optimum outcomes using data visualisation techniques.
  • Realise and assess issues with regards to business ethics dealing with trademark, information safety, honesty, and privacy.
  • Apply moral process in everyday corporate tasks with well-reasoned moral and decisions associated with data management.
  • Thorough understanding of analytical data procedures into business decision making.
  • Evaluating business issues by means of data extraction from data warehouse and then making mining to fix practical concerns.
  • Employ tools and technologies like Big Data, R, Python, Business Analytics, Excel, Probability and Statistics.
  • Make smart use of algorithms to devise machine intelligence.
  • Should become capable of getting involved in team work, lead the team from the front, and become proficient in organisation theory.

Learning Objectives

  • To gain an extensive understanding of how manipulation of data can be done for drawing key conclusions.
  • To have an in-depth comprehension of both non-linear and linear with other data classification techniques.
  • To secure complete understanding of unsupervised and monitoring models including pipeline, clustering, logistic regression, linear regression, and many more.
  • To perform specialised cum scientific computing utilizing SciPy bundle with the likes of Weave, IO, Statistics, and Optimise.
  • To make use of Scikit-Learn and NumPy to do mathematical computing.
  • To understand elements of the Hadoop community.
  • To team up with HBase, its construction, and information storage space, discovering distinction between RDBMS and HBase, using Impala and Hive for partitioning.
  • To get through with MapReduce along with characteristics, plus know how to take in information using Flume and Sqoop.
  • To expert the ideas of recommendation engine backed by time series modelling to have a complete control over machine learning uses, algorithms, and concepts.
  • To analyse data making best use of Tableau to be proficient enough in creating interactive control panels or dashboards.

Course Content

Unit 1: Data Science Basics

  • Organise the cleaned real-world information to use them intelligently.
  • Produce reputable analytical reasonings coming out as a result of unorganised raw information.
  • Utilise machine learning findings to learn versions for information.
  • Envision intricate information.
  • Data Analysis using Apache Spark to evaluate results.

Unit 2: Data Science Tools

  • Use necessary analytical resources like Python and R Programming.
  • Understand essential methods of analytics to apply them.
  • Learn to create a data review pipeline right from collection to storage, analysis, and visualisation.
  • Implement data analytics skill-sets in a given real life practical scenario to optimise your influence.

Unit 3: Data Science Fundamentals

  • Capitalising on Python and R programming skill-sets.
  • Applying analytical principles of modelling, inference, and probability in actual practice.
  • Be well versed with tidyverse to have a strong grip on data wrangling with dplvr and data visualisation with ggplot2.
  • Be familiar with RStudio, GitStudio, Git, and Unix/Linux.
  • Expertise in implementing algorithms dealing machine learning.
  • Comprehensive knowledge of data science concepts linked with information research encouraging real-world cases.

Unit 4: Statistical Data Science

  • Set the right groundwork of data science research studies with learning of the analytical tools.
  • Assess data-driven predictions with probabilistic choices in analytical reasoning to not just recognise but even set up ideal choices for purposeful and insightful decision making.
  • Build algorithms to extract relevant info coming from seemingly disorderly data, know well-known unsupervised strategies and methods for building data clusters, as well as deal with neural networks that are pretty deep.
  • Be an Data Engineer, Data Analyst, Business Intelligence Analyst, Data Analytics Professional, or Data Scientist.

Unit 5: Professional Data Science

  • Apply scientific research for Machine Learning capabilities, strategies, with tools to accomplish data extraction, mining, and analysis for utmost results.
  • Exercise upon high end data science tools used by Scientists to be seasoned in making use of all or few of them like Jupyter notebooks.
  • Tackling data science concerns and adhering to data analysis techniques to be a pro data analyst.
  • Be thorough with Python and R programming making use of Jupyter.
  • Import well-maintained data sets to examine information, create and evaluate data, by utilizing R or Python.
  • Utilise numerous data visualisation tactics, public libraries, and other modules to extract information relevantly.
  • Understand and apply unsupervised as well as supervised machine learning algorithms and formulas to resolve real life challenges making use of Python or R.