Data Science

Course Features

• Instructor Live Sessions

  30hrs of Online Live Instructor-led Classes. Weekend class:10 sessions of 3 hours each and Weekday class:15 sessions of 2 hours each.
• Real-life Case Studies

  Live project based on any of the selected use cases on the above selected Domain.
  
• Assignments

  Each class will be followed by practical assignments which can be completed before the next class.
• 24 x 7 Expert Support

  We have 24x7 online support team available to help you with any technical queries you may have during the course.
• Certification

  Towards the end of the course, you will be working on a project. Covalent certifies you as an course Expert based on the project.

Course Curriculum

• Data Science Course Content

  1. Introduction about Data Science: 

  • What is data science?
  • Need of data science?
  • Use cases of Data science
  • How is data science different from business intelligence?
  • Who are data scientists?

   

  2. Statistics: 

  Section 1: Descriptive Statistics 

  • Intro to Research Methods
  • Central tendency & Variability and Central limit theorem
  • Variance and Range parameters
  • Normal & Sampling distributions

   Section 2: Inferential Statistics 

  • Intro to Predictive modeling
  • Hypothesis testing: t-tests/z-test(1-sample,paired sample)
  • Correlation
  • Regression
  • Chi-square test
  • Analysis of Variance (ANOVA)

  Section 3: practice lab

   

  3. R & Python Programming: 

  Section 1: Environment setup & R/Python language basics: 

  • Application of machine learning
  • Understand Business Analytics and R, Python
  • Knowledge on the R & python language
  • Community and ecosystem
  • Understand the use of 'R & python' in the industry
  • Compare R, Python with other software in analytics
  • Install R, Python and the packages useful for the course
  • Perform basic operations in R, Python using command line
  • Learn the use of IDE R, Python and Various GUI 

  Section 2: Exploratory Data Analysis (EDA) and Data Preprocessing techniques in R/Python language: 

  • Data structures & data types(Vectors, Matrices, Lists, Factors & Data frames/Pandas)
  • Importing Data(from different
  • File formats, Databases, Stats software and web)
  • Exporting Data
  • Viewing Data
  • Handling Missing Values
  • Date & Time
  • Understanding the cor() in R & Python
  • EDA functions like summarize(), list()
  • Multiple packages in R & Python for data analysis
  • The Fancy plots like Segment plot
  • HC plot in R & Python
  • Understanding on Data Visualization
  • Graphical functions present in R & Python
  • Plot various graphs like table plot, histogram, boxplot
  • Customizing Graphical Parameters to improvise the plots
  • How to use ggplot2/matplot libraries

   Section 3: practice lab

   

  4. Machine Learning: 

  Section 1: Classification 

  • Logistic Regression
  • K-Nearest Neighbors (K-NN)
  • Support Vector Machine (SVM)
  • Naive Bayes
  • Decision Tree Classification
  • Random Forest Classification
  • XG Boost Classification
  • Evaluating Classification Models Performance like Confusion matrix, ROC curve, F-Score…

  Section 2: Regression 

  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Support Vector Regression (SVR)
  • Decision Tree Regression
  • Random Forest Regression
  • Evaluating Regression Models Performance like R-Squared and Adjusted R-Squared Intuitions 

  Section 3: Clustering

  • What is Clustering
  • K- Means Clustering
  • Hierarchical Clustering
  • LDA (Latent Dirichlet allocation) 

  Section 4: Recommendation

  • What is Recommendation
  • Association rules using generator
  • Content and Collaborative filtering recommendation techniques
  • Recommendation engine building using R/Python libraries

   

  5. Real Time Projects: 

  Section 1: Real time Projects

  We will teach 2 Real time end to end projects in Health Care and Retail domain industries.

  Section 2: Tableau

  • Introduction about Tableau and building First Bar chart
  • Time series, Aggregation, and Filters
  • Maps, Scatterplots, and Dashboards
  • Joining and Blending Data, PLUS: Dual Axis Charts 

  Section 3: Resume Preparation

Projects

• Data Science Project

  We will provide 2 Real time projects.

FAQ's

• Can I attend a demo session before enrolment?

  Yes

• What if I miss a class ?

  If you miss a class we can provide recording video for particular session and same session you have to attend another batch also

• Will I get placement Assistance ?

  Yes

• Do I receive a certificate for training ?

  • Once you are successfully through the course you will be awarded with Covalent's Training certificate.
  • Covalent certification has industry recognition and we are the preferred training partner for many MNCs.

• what support is available after the training?

  Doubts clarification up to getting a job

  Resume preparation

  Malk interviews

  Placement Assistance

• What Features do you provide?

  • Led sessions with corporate trainers
  • Course Material
  • Real time projects with industry experts
  • Day wise Assignments (Tasks)
  • Lab facilities
  • Placement assistance
  • Resume preparation
  • Software installation
  • Doubts  clarifications

Certifications

• Course Completion Certificate

  • Once you are successfully through the course you will be awarded with Covalent's Training certificate.
  • Covalent certification has industry recognition and we are the preferred training partner for many MNCs.