Business Analytics 360 Course

BY
Analytixlabs

Get trained with the various business analytics tools and develop your skill from the fundamental to the advanced level with the business analytics 360 courses.

Mode

Online

Fees

₹ 32000

Quick Facts

particular details
Collaborators IBM
Medium of instructions English
Mode of learning Self study, Virtual Classroom +1 more
Mode of Delivery Video and Text Based
Frequency of Classes Weekdays, Weekends
Learning efforts 8-10 Hours Per Week

Course overview

The business analytics 360 courses is designed and provided by the online education provider platform Analytixlab partnered with IBM for the dual certification. This online program is scheduled for the students in three different modes that include live sessions, online learning, or self-study. The modules are taught through thirty-three classes which will require the students to spend three-sixty hours.

The course study is about the primary statistical concepts and principles to the advanced level of analytics along with the predictive modeling strategies. The candidates can upskill themselves to establish their career associated with business analytics and counter real-world challenges. 

The interactive sessions and doubt resolution support the students and aid in their experiential learning of data science and business analytics. The business analytics 360-course training helps the candidates gain professional guidance for career development. The course modules consist of assignments and capstone projects, which ought to be completed by the candidates to receive the certification.

The highlights

  • Online mode
  • Interactive virtual sessions
  • Self-study program
  • Recorded lectures
  • Demo session 
  • 360 hours training program
  • 33 classes
  • Capstone projects
  • Classroom and Bootcamp
  • Career guidance
  • Job referrals
  • Profile building
  • Testimonials
  • Certificate

Program offerings

  • Course lectures
  • Recordings
  • Assignments
  • Case study
  • Basic exercises
  • Demo session
  • Technical skills
  • Loan assistance
  • Projects
  • Profile building
  • Job referrals
  • Career guidance
  • Certificate

Course and certificate fees

Fees information
₹ 32,000

The business analytics 360-course training is available for the students in three different modes with the respective amounts of payments and the course fee can be paid in three installments.

Business analytics 360-course fee structure

Weekday Bootcamp

Rs 48000  + taxes

Weekend Batches

Rs 48000  + taxes

Self-paced Blended

Rs 38,000 + taxes

certificate availability

Yes

certificate providing authority

Analytixlabs

Who it is for

The business analytics 360 course is for individuals with qualifications in fields such as engineering, mathematics, finance, and business management. The insights gained from this training will help the students develop their careers in the domain of analytics and data science as business analystsproject managers, or data scientists.

Eligibility criteria

Certificate qualifying details:

The participants of the ‘Business Analytics 360 course’ online program are provided with the Analytixlab certificate after the completion of the mandatory projects and assignments. 

What you will learn

Analytical skills Knowledge of big data Business analytics knowledge Data science knowledge Knowledge of data mining Knowledge of excel Knowledge of python R programming

The Business Analytics 360 course syllabus is framed for the students who have no background knowledge of business analytics to begin their career in the field of analytics. The data science skills associated with data visualization, descriptive and predictive analytics are utilized by the professionals to make wise decisions in business. The Business Analytics 360 course training offers the students insights that aid project management, analyzing risks, evaluation of the digital and social networks and business operations. The analytical skills for the functional domains like banking and finance, retail and e-commerce, pharma, healthcare, telecom, and networks.

The syllabus

Data Visualization and Analytics

Building Blocks
  • Introduction to Bridge Course & Analytics Software’s Basic Excel
  • Basic Programming Elements
  • Introduction to Basic Statistics
  • RDBMS & SQL (Basics)
  • Introduction to Analytics & Data Science
  • Introduction to Mathematical Foundations
Data analytics and visualization with Excel
  • Quick Recap of Basics of Excel
  • Data manipulation using functions
  • Data analysis and reporting
  • Data Visualization in Excel
  • Overview of Dashboards
  • Create dashboards in Excel - Using Pivot controls
  • Business Dashboard Creation
Data analytics with SQL
  • Quick Recap of RDBMS & Basic SQL
  • Data based objects creation (DDL Commands)
  • Data manipulation (DML Commands)
  • Accessing data from Multiple Tables using SELECT
  • Advanced SQL 
  • Apply learning's on Business Case study
Data analytics and visualization with tableau
  • Getting Started
  • Data handling & summaries
  • Building Advanced Reports/ Maps
  • Calculated Fields
  • Table calculations
  • Parameters
  • Building Interactive Dashboards
  • Building Stories
  • Working with Data
  • Sharing work with others
Data analytics with VBA
  • Introducing VBA
  • How VBA Works with Excel
  • Key Components of Programming language
  • A look at some commonly used code snippets
  • Programming constructs in VBA
  • Functions & Procedures in VBA – Modularizing your programs
  • Objects & Memory Management in VBA
  • Error Handling
  • Controlling accessibility of your code – Access specifiers
  • Code Reusability – Adding references and components to your code
  • Communicating with Your Users

R for Data Science (Optional eLearning)

Data Importing/Exporting
  • Introduction R/R-Studio - GUI
  • Concept of Packages - Useful Packages (Base & Other packages)
  • Data Structure & Data Types (Vectors, Matrices, factors, Data frames, and Lists) 
  • Importing Data from various sources
  • Exporting Data to various formats
  • Viewing Data (Viewing partial data and full data)
  • Variable & Value Labels –Date Values
Dimensionality Reduction & Collaborative Filtering
  • Dimensionality Reduction: Feature Extraction & Selection
  • Collaborative Filtering & Its Challenges
Data Manipulation
  • Creating New Variables (calculations & Binning)
  • Dummy variable creation
  • Applying transformations
  • Handling duplicates/missing's
  • Sorting and Filtering
  • Sub setting (Rows/Columns)
  • Appending (Row/column appending)
  • Merging/Joining (Left,right,inner,full,outer)
  • Data type conversions
  • Renaming
  • Formatting
  • Reshaping data
  • Sampling
  • Operators
  • Control Structures (if, if else)
  • Loops (Conditional, iterative loops)
  • apply functions
  • Arrays
  • R Built-in Functions
  • Text, Numeric, Date, utility
  • R User Defined Functions
  • Aggregation/Summarization
Data Analysis
  • Introduction exploratory data analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Uni-variate Analysis (Distribution of data)
  • Bivariate Analysis (Cross Tabs, Distributions & Relationships)
Using R withDatabases
  • R and Relational Databases
  • Connecting to Relational Databases using RJDBC and RODBC
  • Database Design and Querying Data
  • Modifying Data and Using Stored Procedures
  • In-Database Analytics with R
Data Visualization with R
  • Basic Visualization Tools
    • Bar Charts/Histograms/Pie Charts
    • Scatter Plots
    • Line Plots and Regression
  • Specialized Visualization Tools
    •  Word Clouds/ Radar Charts
    •  Waffle Charts/ Box Plots
  • How to create Maps
    • Creating Maps in R
  • How to build interactive web pages
    •  Introduction to Shiny
    •  Creating and Customizing Shiny Apps
    •  Additional Shiny Features
Introduction to Statistics
  • Basic Statistics - Measures of Central Tendencies and Variance
  • Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem
  • Inferential Statistics -Sampling - Concept of Hypothesis Testing
  • Statistical Methods - Z/t-tests (One sample, independent, paired), Anova, Correlations and Chi-square
Linear Regression: Solving regression problems
  • Introduction - Applications
  • Assumptions of Linear Regression
  • Building Linear Regression Model
  • Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis, etc)
  • Assess the overall effectiveness of the model
  • Validation of Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
  • Interpretation of Results - Business Validation - Implementation on new data
Supervised Learning I
  • K-Nearest Neighbors
  • Decision Trees
  • Random Forests
  • Reliability of Random Forests
  • Advantages & Disadvantages of Decision Trees
Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning
  • Machine Learning Languages, Types, and Examples
  • Machine Learning vs Statistical Modelling
  • Supervised vs Unsupervised Learning
  • Supervised Learning Classification
  • Unsupervised Learning
Supervised Learning II
  • Regression Algorithms
  • Model Evaluation
  • Model Evaluation: Overfitting & Underfitting
  • Understanding Different Evaluation Models
Unsupervised Learning
  • K-Means Clustering plus Advantages & Disadvantages
  • Hierarchical Clustering plus Advantages & Disadvantages
  • Measuring the Distances Between Clusters - Single Linkage Clustering
  • Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering
  • Density-Based Clustering

Python for Data Science

Python Essentials (Core)
  • Overview of Python- Starting with Python
  • Why Python for data science?
    • Anaconda vs. python
  • Introduction to installation of Python
  • Introduction to Python IDE's(Jupyter,/Ipython)
  • Concept of Packages - Important packages
    • NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc
  • Installing & loading Packages & Name Spaces
  • Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries) 
  • List and Dictionary Comprehensions
  • Variable & Value Labels –Date & Time Values
  • Basic Operations – Mathematical/string/date
  • Control flow & conditional statements
  • Debugging & Code profiling
  • Python Built-in Functions (Text, numeric, date, utility functions)
  • User defined functions – Lambda functions
  • Concept of apply functions
  • Python – Objects – OOPs concepts
  • How to create & call class and modules?
Operations with NumPy (Numerical Python)
  • What is NumPy?
  • Overview of functions & methods in NumPy
  • Data structures in NumPy
  • Creating arrays and initializing
  • Reading arrays from files
  • Special initializing functions
  • Slicing and indexing
  • Reshaping arrays
  • Combining arrays
  • NumPy Maths
Overview of Pandas
  • What is pandas, its functions & methods
  • Pandas Data Structures (Series & Data Frames)
  • Creating Data Structures (Data import – reading into pandas)
Cleansing Data with Python
  • Understand the data
  • Sub Setting / Filtering / Slicing Data
    • Using [] brackets
    • Using indexing or referring with column names/rows
    • Using functions
    • Dropping rows & columns
  • Mutation of table (Adding/deleting columns)
  • Binning data (Binning numerical variables in to categorical variables)
  • Renaming columns or rows
  • Sorting (by data/values, index)
    • By one column or multiple columns
    • Ascending or Descending
  • Type conversions
  • Setting index
  • Handling duplicates /missing/Outliers
  • Creating dummies from categorical data (using get_dummies())
  • Applying functions to all the variables in a data frame (broadcasting)
  • Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc.)
Data Analysis using Python
  • Exploratory data analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Uni-variate Analysis (Distribution of data & Graphical Analysis)
  • Bi-Variate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
Data Visualization with Python
  • Introduction to Data Visualization
  • Introduction to Matplotlib
  • Basic Plotting with Matplotlib
  • Line Plots
Basic Visualization Tools
  • Area Plots
  • Histograms
  • Bar Charts
  • Pie Charts
  • Box Plots
  • Scatter Plots
  • Bubble Plots
Advanced Visualization Tools
  • Waffle Charts
  • Word Clouds
  • Seaborn and Regression Plots
Visualizing Geospatial Data
  • Introduction to Folium
  • Maps with Markers
  • Choropleth Maps
Statistical Methods & Hypothesis Testing
  • Descriptive vs. Inferential Statistics
  • What is probability distribution?
  • Important distributions (discrete & continuous distributions)
  • Deep dive of normal distributions and properties
  • Concept of sampling & types of sampling
  • Concept of standard error and central limit theorem
  • Concept of Hypothesis Testing
  • Statistical Methods - Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi- square

Predictive Modelling and Introduction to ML

Introduction to Predictive Modeling & ML
  • Concept of model in analytics and how it is used?
  • Common terminology used in modeling process
  • Types of Business problems - Mapping of Algorithms
  • Different Phases of Predictive Modeling
  • Data Exploration for modeling
  • Exploring the data and identifying any problems with the data (Data Audit Report)
  • Identify missing data
  • Identify outliers data
  • Visualize the data trends and patterns
Linear Regression: Solving regression problems
  • Introduction - Applications
  • Assumptions of Linear Regression
  • Building Linear Regression Model
  • Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis, etc)
  • Assess the overall effectiveness of the model
  • Validation of Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
  • Interpretation of Results - Business Validation - Implementation on new data
Logistic Regression: Solving classification problems
  • Introduction - Applications
  • Linear Vs. Logistic Regression Vs. GLM
  • Building Logistic Regression Model (Binary Logistic)
  • Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
  • Validation of Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
  • Interpretation of Results - Business Validation - Implementation on new data
Industrial & Functional Sessions (Domain Understanding)
  • Introduction to Data Sources for Various Industries
  • Introduction to Analytics Project Management
  • Marketing Analytics
  • Risk Analytics
  • Operation Analytics
  • Digital Analytics (Web Analytics)
  • Social Network Analytics
  • Banking & Financial Services, Insurance
  • Retail & E-Commerce
  • Pharma & Health Care
  • Telecom & Network

Placement Readiness Program

Placement Readiness Program
  • CV preparation and expert sessions
  • Profile creation on Kaggle and GitHub
  • Mock interviews 
  • Personal interviews focusing on effective communication and behavioral fit
  • Technical interviews on applications of data science concepts and techniques
  • Placement days - Actual & Practice Runs
  • Technical tests - MCQs and Hiring Projects/ Case Studies
  • Case Study Presentations
  • Interviews with Industry Experts
  • General Analytics and Problem Solving
  • Profile optimization on job portals (like LinkedIn, Naukri, Indeed, IIMJobs, etc.)
  • Continual feedback sessions pre and post interviews

Admission details

The admission procedure for the ‘Business Analytics 360 course’ is done online through the Analytixlab website.

Step 1: Go to the course page on the official website of Analytixlab using the link below: https://www.analytixlabs.co.in/business-analytics-data-science-course

Step 2: Choose among the payment options provided and click the respective ‘Enroll Now’ link

Step 3: Fill in the relevant information and complete the registration.


Filling the form

The candidates will have to enter their name, phone number, email address, course name, and city name in the business analytics 360-course registration form.

How it helps

The business analytics 360-course certification enables the candidates to upskill themselves for a career in the field of analytics. The certification helps them in gaining better job opportunities in the data analytics domain and enhances their business decision-making process with the help of the insights acquired. The trainees of this course will gain skills based on MIS reporting analytics, data visualization, data blending, and manipulation, for data mining and analysis along with the techniques of R programming. The business analytics 360 course benefits the students by improving their knowledge on statistical analysis and modeling, predictive modeling, using the software Python for data analysis and reporting analytics.

FAQs

What is the duration of the business analytics 360 course provided by Analytixlab?

The course modules and lectures will take up to 360 hours to complete and the candidates are provided with the flexibility to finish the course in one year.

What is the eligibility for the business analytics 360 course training?

There is no mandatory prerequisite but the students with knowledge in domains like engineering, mathematics, finance, and business management will benefit from the course.

Can I get loan assistance to pay the business analytics 360 course fee?

Yes, there is a facility for loan sanction provided for the students.

Will I get a course completion certificate after finishing the business analytics 360 course?

Yes, after successful completion and evaluation of assignments and projects without plagiarism, you will receive a certificate.

How many projects are included in the business analytics 360 course training?

The course included nine projects.

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