Professional Certificate Programme in Advanced Data Analytics for Managers
100% Job Assistance In Professional Certificate Program in Advanced Data Analytics for Managers with 0-3 Years of Work Experience
- Mode of learning: Live online sessions
- Duration: 4 months
- Class schedule: Weekend
Advance your career with the Certification in Advanced Data Analytics for Managers
In a world where data drives decisions, mastering analytics is no longer optional—it’s essential. The Advanced Data Analytics for Managers, offered by Edification Union, empowers business leaders, managers, and professionals with the tools and techniques to turn data into strategic advantage.
Designed and delivered by India’s top faculty, this 12-month immersive learning experience bridges the gap between business acumen and data science, transforming professionals into data-savvy decision-makers.
Whether you’re looking to sharpen your analytics skills, take on leadership roles, or future-proof your career, this program will equip you with the power to lead in a data-first world.
Programme Highlights
Live Weekend Classes
Learn through interactive online sessions every Saturday Sunday.
Real-World Focus
Solve case studies and apply analytics to business problems.
Hands-On Tools
Gain practical experience with R, Python, Tableau, Power BI, and Excel.
Capstone Project
Work on a faculty-guided project to demonstrate your analytical skills.
Industry-Ready Curriculum
Learn data cleaning, EDA, visualization, statistical inference, ML models, and text mining.
Executive Alumni Benefits
Join an exclusive network of professionals post-completion.
Who Is This Programme For?
Early to Mid-Level Finance Professionals
Learn through interactive online sessions every Saturday Sunday.
Business Analysts, Investment Analysts, and Risk Managers
who want to deepen their understanding of applied financial principles.
Entrepreneurs and FoundersOn Tools
who need to sharpen their financial decision-making capabilities.
Working Professionals
from Non-Finance Backgrounds aspiring to gain a solid grasp of finance to accelerate career growth or pivot roles.
MBA Graduates and Aspiring CFOs
looking to future-proof their careers with an IIM-backed certification and industry-relevant expertise.
Program Objective
Equip participants with a deep understanding
of data analytics tools, techniques, and applications relevant to real-world business challenges.
Bridge the knowledge gap
between traditional management practices and modern data science methodologies.
Enable professionals
to interpret complex data, derive actionable insights, and make data-backed strategic decisions.
Foster analytical thinking
to solve business problems across domains such as marketing, finance, operations, and HR.
Prepare participants
to lead digital transformation initiatives and become future-ready managers in a data-intensive economy.
Program Curriculum
- Introduction to the R Environment
- IDE-R Studio
- Installing Packages
- Loading Packages
- Creating Variables
- Scalars, Vectors and Matrices
- List, Data Frames and Data Types
- Converting Between Vector Types
- Cbind and
- Rbind
- Attach and Detach Functions
- Reading .csv and .txt Files
- Importing Data from Excel
- Loading and Storing Data with a Clipboard
- Saving in R Data, Loading R Data Objects
- Writing Data into the File
- Writing Text and Output from Analysis to File
- Rmarkdown
- Data Subsets
- Selecting Rows/observations
- Rounding a Number
- Creating a String from Variable
- Factor Labels
- Selecting Columns/fields
- Merging Data
- Relabelling the Column Names
- Data sorting, Data Aggregation, and Finding and Removing Duplicate Records
- Application of dplyr Package (select, arrange, mutate, aggregate, summarise, and group)
Note:
- Modules/ topics are indicative only, and the suggested time and sequence may be dropped/ modified/ adapted to fit the overall
program needs.
- Schedule will be announced closer to programme start. The recorded videos and learning material will be available throughout the duration of the programme
- Basics of Data Visualisation using ggplot2
- Aesthetic Mappings
- Common Problems
- Facets
- Geometric Objects
- Position Adjustments
- Coordinate Systems
- The Layered Grammar of Graphics
- Combining Plots
- Execution of Various Types of Plots (box plot, histogram, pie chart, line chart, scatterplot, word
cloud, probability plots, mosaic plots, correlograms, and interactive graphs)
- Data Cleaning
- Handling Missing Data
- Data Imputation
- Feature Filtering
- Categorical Feature Filtering
- Identifying Misclassification
- Data Transformation
- Min-max Normalisation
- Z-score
- Standardisation
- Decimal Scaling
- Transformations to Achieve Normality
- Outliers
- Graphical Methods for Identifying Outliers
- Numerical Methods for Identifying Outliers
- Flag Variables
- Transforming Categorical Variables into Numerical Variables
- Binning Numerical Variables Reclassifying Categorical Variables
- Adding an Index Field
- Removing Variables that are not useful
- Data Balancing Techniques
Note:
- Modules/ topics are indicative only, and the suggested time and sequence may be dropped/ modified/ adapted to fit the overall
program needs.
- Schedule will be announced closer to programme start. The recorded videos and learning material will be available throughout the duration of the programme.
- Hypothesis Testing versus Exploratory Data Analysis
- Getting to Know the Data Set
- Exploring Categorical Variables
- Exploring Numeric Variables
- Exploring Multivariate Relationships
- Selecting Interesting Subsets of the Data for Further Investigation
- Using EDA to Uncover Anomalous Fields
- Binning Based on Predictive Value
- Deriving New Variables: Flag Variables
- Deriving New Variables: Numerical Variables
- Using EDA to Investigate Correlated Predictor Variables
- Need for Dimension-Reduction in Data Mining
- Principal Components Analysis (PCA)
- Application of PCA
- Statistical Inference
- Confidence Interval Estimation of the Mean
- The Margin of Error
- Confidence Interval Estimation of the Proportion
- Hypothesis Testing for the Mean
- Assessing the Strength of Evidence Against the Null Hypothesis
- Using Confidence Intervals to Perform Hypothesis Tests
- One-sample T-test
- Paired Sample T-test
- Chi-square Test for Goodness of Fit of Multinomial Data
- Analysis of Variance (ANOVA)
Note:
- Modules/ topics are indicative only, and the suggested time and sequence may be dropped/ modified/ adapted to fit the overall
program needs.
- Schedule will be announced closer to programme start. The recorded videos and learning material will be available throughout the duration of the programme.
- Supervised Versus Unsupervised Methods
- Statistical Methodology and Data Mining Methodology
- Cross-validation
- Overfitting
- Bias-variance Trade-off
- Balancing the Training Data set
- Establishing Baseline Performance
- Simple Regression Analysis
- Model Formulation
- Verifying the Regression Assumptions
- Inference in Regression
- Multiple Regression Analysis
- Dummy Variable
- Stepwise Regression Analyses
- k-nearest Neighbour Algorithm
- Decision Tree Random Forest
- Neural Networks for Estimation and Prediction
- Application of Logistic Regression for Estimation and Prediction
- Naïve bayes and Bayesian Networks
- Hierarchical Clustering Methods
- k-Means Clustering
- Measuring Cluster Goodness
- Affinity Analysis
- Market Basket Analysis
Note:
- Modules/ topics are indicative only, and the suggested time and sequence may be dropped/ modified/ adapted to fit the overall
program needs.
- Schedule will be announced closer to programme start. The recorded videos and learning material will be available throughout the duration of the programme.
- Text Mining and Sentiment Analysis
- Social Media Analytics (Twitter)
- Lexicon Analysis
- Social Network Analysis
Join Edification Union graduates at top-tier companies
EMI options are available
Book Your Seat With
INR 15,000 only
Balance Payment Can Be Paid in 2 Instalments
Book Your Seat With
INR 15,000 only
Balance Payment Can Be Paid in 2 Instalments
