- Introduction to Business Analytics
- R for Data Science
- Introduction to R and R-Studio
- Dealing with Data using R
- Visualization using R
Online
12 Months
Quick facts
particular | details | |
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Medium of instructions
English
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Mode of learning
Self study, Virtual Classroom
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Mode of Delivery
Video and Text Based
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Course overview
The world is surrounded by data. Every day, the volume and diversity of data that we gather keep growing, from basic retail transaction data to millions of sophisticated and private medical records. There is a rising need for individuals who can regulate and manage the usage of data. The Masters in Data Science and Analytics online course provides powerful analysis training to candidates to instill the capacity to draw insights from massive data and create automated artificial intelligence systems.
The Masters in Data Science and Analytics training is extremely practical, incorporating business strategy, project-based learning, case studies, particular electives, and simulations addressing the analytical needs of various business areas. Collaborations with corporate partners provide learners with access to actual data sets as well as the opportunity to focus on contemporary business challenges.
The Masters in Data Science and Analytics syllabus delves deeply into advanced techniques, data-gathering, and data analytics technologies. This program enables students to develop their multidisciplinary abilities while also gaining a deep understanding of applied and scientific expertise in analytics and data science.
The highlights
- Multiple Simulation Exams
- Industrial Projects
- Industry Based Trainers
- Student Handouts
- Job Assistance
- 12 Months classes
- Course Completion Certificate
- Live Online lectures
Program offerings
- Assignments
- Online learning
- Examinations
- Videos
- Surprise tests
- Capstone projects
- Notes
- Mock papers
Course and certificate fees
certificate availability
certificate providing authority
Eligibility criteria
- Graduation in any discipline
- 0-5 years of professional experience(Freshers can also apply)
Certification Qualifying Details
To qualify for the Masters in Data Science and Analytics certification, candidates pass an exam conducted by Careerera at the end of the course.
What you will learn
After completing the Masters in Data Science and Analytics online training from Careerera, students will learn about SQL and Python to manage and mine data. Candidates will learn Tableau for data visualization, Excel for statistical data analysis along predictive statistical modeling.
Who it is for
The Masters in Data Science and Analytics online course will be beneficial to candidates who want to become Data Scientist, Quantitative Analyst, Data Analyst, Business Analyst, etc.
Admission details
To get admission to the Masters in Data Science and Analytics tutorial, follow the steps mentioned below:
Step 1. Open the official course page by following the link below
(https://www.careerera.com/data-science/masters-in-data-science-and-analytics)
Step 2. Apply online by filling out the application form on the website.
Step 3. The faculty committees will review all applications and choose applicants based on their qualifications.
Step 4. Selected candidates will be subjected to an interview, which will be graded by a committee of faculty members.
Step 5. The shortlisted individuals will subsequently be sent an admission proposal.
The syllabus
Introduction to Analytics
- Analysis of Variance
- Regression Analysis
- Dimension Reduction Techniques
Statistical Method for Decision Making
- Descriptive Statistics
- Introduction to Probability
- Probability Distributions
- Hypothesis Testing and Estimation
- Goodness of Fit
Business Finance
- Fundamentals of Finance
- Working Capital Management
- Capital Budgeting
- Capital Structure
Marketing and CRM
- Core Concepts of Marketing
- Customer Lifetime Value
Data Mining
- Introduction to Supervised and Unsupervised Learning
- Clustering
- Decision Trees
- Random Forest
- Neural Networks
Predictive Modeling
- Multiple Linear Regression
- Logistic Regression
- Linear Discriminant Analysis
Time Series Forecasting
- Introduction to Time Series
- Correlation
- Forecasting
- Autoregressive models
Machine Learning
- Handling Unstructured Data
- Machine Learning Algorithms
- Bias Variance trade-off
- Handling Unbalanced Data
- Boosting
- Model Validation
Optimization Techniques
- Linear programming
- Goal Programming
- Integer Programming
- Mixed Integer Programming
- Distribution and Network Models
Marketing and Retail Analytics
- Marketing and Retail Terminologies
- Customer Analytics
- KNIME
- Retail Dashboard
- Customer Churn
- Association Rules Mining
Web & Social Media Analytics
- Web Analytics: Understanding the metrics
- Basic & Advanced Web Metrics
- Google Analytics: Demo & Hands-on
- Campaign Analytics
- Text Mining
Finance & Risk Analytics
- Why Credit Risk-Using a market case study
- Comparison of Credit Risk Models
- Overview of Probability of Default (PD) Modeling
- PD Models, types of models, Steps to make a good model
- Market Risk
- Value at Risk - using a stock case study
Supply Chain & logistics Analytics
- Introduction to Supply Chain
- Dealing with Demand uncertainty
- Designing Optimal Strategy using Case Study
- Inventory Control & Management
- Inventory classification
- Inventory Modeling
- Costs Involved in Inventory
- Economic Order Quantity
- Forecasting
- Advanced Forecasting Methods
- Examples & Case Studies
- Visualization and insights
Data Visualization using Tableau
- Introduction to Data Visualization
- Introduction to Tableau
- Basic charts and dashboard
- Descriptive Statistics, Dimensions, and Measures
- Visual analytics: Storytelling through data
- Dashboard design & principles
- Advanced design components/ principles: Enhancing the power of dashboards
- Special chart types
- Case Study: Hands-on using Tableau
- Integrate Tableau with Google Sheets
Languages and tools
- R and Python
- Tableau
- SAS (Online Module)
- Hackathons
Capstone project
Group Presentation
How it helps
Candidates pursuing Masters in Data Science and Analytics course will be benefited in the following ways:
- Improve Decision-Making Procedures (significance and quality) to accelerate the process of decision-making.
- Improve the alignment with strategy and cost-effectiveness
- Increase competitiveness and revenue
- Create a unified view of corporate information and sync operational and financial strategies.
- Learn to disseminate knowledge to a larger audience.
FAQs
The Carrera is the course provider of the online Masters in Data Science and Analytics course.
Yes, candidates can change the batch of Masters in Data Science and Analytics according to their availability and convenience.
Yes, candidates will have to appear and pass an examination to qualify for Masters in Data Science and Analytics certification.
The duration of the Masters in Data Science and Analytics training is 12 months.
There are various career scopes after completing the Masters in Data Science and Analytics program such as Business analyst, Data analyst, Data scientist, etc.