Data Science Training

OVERVIEW
Building Strategic Influence in Matrix Organizations

Data Science Certification

The Data Science course enables you to understand practical foundations, helping you effectively execute and take up Big Data and other analytics projects. The program covers topics from Big Data to the Data Analytics Life Cycle. Understanding these topics helps in addressing business challenges that leverage Big Data.

Another aspect of this course is that it covers basic as well as advanced analytic methods, and also introduces the participant to Big Data technologies with tools like MapR and Hadoop. Our state-of-the-art-infrastructure allows students to understand the applications of these methods and tools by getting hands-on experience working alongside real-time data scientists. This program has an open approach including a final lab session, which explains various Big Data Analytics challenges by applying the concepts covered during the program with respect to the Data Analytics Life Cycle.

Who can learn Data Science?

The course is designed for anyone who wishes to understand the concepts of Data Science from a Data Scientist’s perspective. Professionals who can benefit from this course include:

Managers from any field, as Analytics is the best tool for managers these days
Business Analysts and Data Analysts who wish to upscale their Data Analytics skills
Database professionals who aspire to venture into the field of Big Data by acquiring analytics skills
Fresh graduates who wish to make a career in the field of Big Data or Data Science

WHAT YOU'LL LEARN
Why you shouldn't miss this course

By the end of this course, participants will have the leadership toolkit to shape and steer GenAI portfolios across their organization.

01Be a part of a data science team and work on Big Data and various other analytics projects
02Deploy the Data Analytics Life Cycle for Big Data projects
03Change the frame of a challenge from a business perspective to analytics
04Understand which analytics techniques and tools will work in a specific Big Data analysis
05Create statistical models and understand which insights can lead to actionable results
06Select appropriate data visualizations, which would help in communicating analytics insights to business sponsors and analytics audience in a clearer manner
07Use various Big Data tools like Hadoop, MapR, R, In-Database Analytics, and MADLib functions
08Understand how to leverage advanced analytics to create a competitive advantage, and how the roles of data scientists and BI analysts are different from each other

PREREQUISITES
Recommended experience

CURRICULUM
Structured for
Strategic Application
  • What is Data Science?
  • Skill set required
  • Job opportunities
  • Continuous vs. Categorical variables
  • Mean, Median, Mode, Standard Deviation, Quartile, IQR
  • Hypothesis testing, z-test, t-test

Installation of R Studio

  • Overview of R Studio components
  • Data Structures
  • Vector
  • List
  • Matrices
  • Data Frame
  • Factor
  • Slicing and Sub-setting
  • Vector
  • List
  • Matrix
  • Data Frame

Functions in R

  • In-built functions
  • User-defined functions

Loops in R

  • while
  • for
  • break
  • next

Data Import in R

Apply Family of Functions

  • lapply
  • sapply
  • tapply

Data Manipulation Using dplyr

Data Visualization Using ggplot2

What is Machine Learning?

Supervised vs. Unsupervised Learning

Exploratory Data Analysis

  • Univariate analysis
  • Boxplot
  • Bivariate analysis
  • Scatterplot
  • Correlation
  • Outliers
  • Remove duplication
  • Missing value imputation

Underfitting vs. Overfitting

Linear Regression

  • Simple
  • Multiple
  • Assumptions of Linear Regression
  • Evaluating Accuracy of model: k-Fold Cross validation

Logistic Regression

  • Confusion Matrix
  • ROC Curve

Time Series Forecasting

  • Moving Average
  • Exponential smoothing
  • Holt Winter’s
  • ARIMA
  • Naïve Bayes
  • Support Vector Machine
  • K-Nearest Neighbor
  • Decision tree
  • Random Forest
  • K-Means Clustering
  • Introduction to Big Data
  • Overview of Hadoop & its Ecosystem
  • Introduction to NoSQL
  • Overview of Apache Spark

RECOMMENDED PARTICIPANT SETUP
This course follows Cognixia's AI-first,
hands-on learning model

Access to sanitized process maps, KPI definitions, candidate initiative lists, and basic cost baselines (time, cycle time, error or rework rates)

INTERESTED IN THIS COURSE?
Let's Connect

Speak with a Cognixia specialist about enrollment options, custom cohorts for your leadership team, or tailored delivery formats for your organization.

Response within 1 business day
Available in 5 delivery formats globally
Volume pricing for teams of 10+
Get in touch

One of our specialists will contact you within one business day.

FAQs
Frequently
Asked Questions

Find details on duration, delivery formats, customization options, and post-program reinforcement.

To attend the live virtual training, one would require at least 2 Mbps of internet speed.

Yes, Cognixia’s Virtual Machine can be installed on any local system. Cognixia’s training team will assist you with this.

To install the Hadoop environment, 8GB RAM, 64-bit OS, 100 GB free space on hard disk, and a Virtualization Technology-enabled processor is required within your system.

The Hadoop Administration course at Cognixia is a 6-week course.

The recorded session for the class will be available on the LMS for your reference. We also have a support team, in case you need any clarification on concepts or help in debug or installation, etc.

The access to the Learning Management System will be for a lifetime.

WHY COGNIXIA
Why Cognixia for This Course

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READY TO SHAPE YOUR AI FUTURE?
Let's build the workforce
of the future

Enroll your leadership cohort in Designing GenAI Use-Case Portfolios & Business Cases.
Custom cohorts available for enterprise teams.