Business Analytics Training in Mumbai

Business Analytics Training Course In Mumbai.

Business Analytics (BA) and its related terms such as Business Intelligence, Big Data, Data Mining,Data Science, etc. have become a powerful tool for growth in the 21st Century. Accenture report of January 2019 says India can add $1.97 trillion in GDP growth if it can bridge skill gap in advance technologies such as Business Analytics and Artificial Intelligence. No wonder Meul Tech is the best Business Analytics training institute in Mumbai

Why You Should Learn Business Analytics ?

Business Analytics is a combination of Data Analytics, Business Intelligence and Computer Programming. It is the science of analyzing data to find out patterns that will be helpful in developing strategies. Its usage can be found in almost every industry.

 

 

WHAT YOU WILL LEARN FROM THIS BUSINESS ANAlYTICS COURSE:

• Understand the fundamentals of statistics.
• How to plot different types of data.
• Calculate correlation and covariance.
• Make data driven decisions.
• Understand the concepts needed for data science even with Python and R!
• Learn how to work with different types of data
• Calculate the measures of central tendency, asymmetry, and variability
• Distinguish and work with different types of distributions
• Perform hypothesis testing
• Understand the mechanics of regression analysis
• Use and understand dummy variables

 

PREREQUISITES:

• Anyone can learn Business Analytics. You just need to work on live projects with us to gain experience.

 

Business Analytics Training
Module-I Excel Basics and Excel for Data Analysis	Introduction to MS Excel Why MS Excel. Functionalities for a Data Scientist. Using Excel. Data Analysis in Excel Basic Data Manipulation Functions(Mean, Maximum, Round, Sum etc) Statistical Functions. Filter, Sort, Lookup in Excel. Using Pivots in Excel.(Creating Pivot Tables and Charts,Usage of Slicers) Plotting in Excel – Usage of Visualization Capabilities
Module-II R Basics and R for Data Analysis	Introduction to R Programming R Studio. What is an Open Source and What is R? Capabilities of R. GUI for R/ R Studio. Installation and Functioning of R and R Studio Using R. Using R. R Interface, R Session, R Console Getting Help. Entering and Running Commands/ Programs. Programming in R. Data Types/ Operators/ Data frames in R. Data Input and Output. R statistics – Mean, Median, Mode etc. Data Manipulation in R – Counting, Merging, Append, Sort, Subset, Filter, New. Variable creation and Logical Statements – If/ else, Loops etc. Plotting- Graphs and Charts. Packages in R- Details of the most commonly used packages. Functions in R (High Level) and Best Practices.
Module-III Statistics	What is Statistics. Data Types. Qualitative vs. Quantitative Basic Operations Based on Data Type. Variables. Measurement Scales. Measures of Variance. Measures of Central Tendency. Correlation vs. Causation (Correlational vs. Experimental Research). Sampling – Usage of Sampling. Distributions. Normal Distribution. Why the “Normal distribution” is Important. Illustration of How the Normal Distribution is Used in Statistical Reasoning. Characteristics. Standard Normal Distribution. Central Limit Theorem. Hypothesis Testing. What is Hypothesis Testing? The Magic of Hypothesis Testing. Null and Alternate Hypothesis. P Value. Usage of Hypothesis Testing in Business Problems. Explanation of Hypothesis Testing Using Real World Example. Types of Hypothesis Testing. Z test. T Test. Chi Square Test.
Module-IV Linear Regression topic details	Graphical Understanding of Regression (Scatter Plot, Box Plot, Density Plot). Example Problem and Mathematics behind Regression. Assumptions for Linear Regression. Correlation (Linear and Non-Linear). Introduction to Multiple Linear Regression. Building a Regression Model (Steps to Establish a Regression). Data Preparation – Data Audit, Missing Value and Outliers. Building the model. Linear Regression – Interpretation of Output and Diagnostics. Assessing the Coefficients. P Value – Checking for Statistical Significance. R-Square and Adjusted R-Square. Standard Error and F-Statistic. Using Linear Model for Predictions. Checking Accuracy and Error Rates. Heteroskedasticity. Model Improvement. Over-fitting and Cross-validation. Multicollinearity and VIF. Do It Yourself Case. Flavor of Advanced Regression Models.
Module-V Logistic Regression	Why Logistic Regression. Introduction to Classification and Challenges with Linear Regression. Event Rate and Class Bias. Example Problem (Some real world examples of Binary. Classification problems), Mechanics and Mathematics behind. Logistic Regression. Assumptions for Logistic Regression. Building a Logistic Regression Model. Data Preparation – Data Audit, Missing Value and Outliers. Variable Importance and Feature Extraction. Create WOE for Categorical Variables. Compute Information Value. Building Logit Models. Predictions. Logistic Regression – Interpretation of Output. Coefficients. Variable Importance. Model Diagnostics. Misclassification Error and Confusion Matrix. ROC Curve. Accuracy. Specificity, Sensitivity and F Score. Lift/Gain Charts and KS Curve. Model Improvement. Over-fitting and Cross-validation. Flavor of Advanced Classification Concepts – Classification of. Unstructured Data. Do It Yourself Case.
Module-VI(Part-I) Time Series Modelling	Introduction to Time Series. Difference between Time Series, Cross-sectional and Pooled Data. Example Problems (Some real-world examples of Time Series Problems), Mechanics and Fundamental of Mathematics behind the Time Series Analysis. Assumptions for Time Series analysis. Understanding Time Series Data. Visualizing Time Series Data. Stationary vs. Non-stationary Data. Trend vs Seasonality vs White Noise. Decomposing Time Series Data. Decomposing Non-seasonal Data. Decomposing Seasonal Data. Seasonality Adjustment. Forecasts Using Exponential Smoothing. Simple Exponential Smoothing. Holt’s Exponential Smoothing. Holt-Winters Exponential Smoothing. Challenges with Smoothing.
Module-VI (Part-II) Time Series Modelling	ARIMA Models. Concept of Auto-Correlation and Partial Auto-Correlation. Differencing a Time Series. Selecting a Candidate ARIMA Model. Forecasting Using an ARIMA Model. Predictions and Diagnostics. Advanced Time Series Concepts. Do it Yourself Case.
Module-VII Market Basket Analysis	Supervised, Unsupervised and Semi-supervised Algorithms. Concept of a Recommendation Engine. Example Problems (Real-world examples of MBA applications). MBA Hyper Parameters. Lift. Confidence. Support. Generating output using Association rules. Filtration of Rules. Removal of Redundant Rules. Control the Rules. Finding rules for Particular Entity. Visualizing Rules. Advanced Recommendation Engine Concepts. Do It Yourself Case.
Module-VIII Decision Trees	Type of Classification Algorithms. Fundamentals of Tree based Systems. Decision Boundary of Tree based Algorithms. Types of Tree Algorithms. C4.5. CHAID. CART. Concept of Impurity measure. GINI. Chi Square. Entropy. Building a Decision Tree Model. Data Preparation – Data Audit, Missing Value and Outliers. Creating Test and Training Samples. Variable Importance and Feature Extraction. Prediction Using Decision Trees. Over-fitting and Cross-validation. Flavor of Advanced Concepts in Trees (Random Forests).
Module-IX K-Means Clustering	Unsupervised Algorithms and Introduction to Clustering. Intro to Distance based Algorithms. Example Problems (Some real-world examples of Clustering Applications). Assumptions for Clustering. Mechanics of Clustering. Type of Measure- Euclidean, Manhattan, Jaccard. How are Clusters Generated? Using Clustering as a Base for Segmentation. Creating Clusters. Standardization of Inputs. Deciding the Number of Clusters – Elbow Curve and Silhouette Distance. Understanding the Output. Cluster Diagnosis. Profiling. Advanced Clustering Concepts. Do It Yourself Case.
Module-X Analytics in Python	Understanding Python. Installing Python through Anaconda. Jupyter Notebook. Python Functions. Python Data types. Tuples. Lists. Dictionaries. Understanding the Data. Python Packages for Data Science. Importing and Exporting Data. Getting Started Analyzing Data. The DataFrame Data Structure. DataFrame Indexing and Loading. Querying a DataFrame. Group by. Scales. Pivot Tables. Dealing with Missing Values. Data Formatting. Data Normalization. Categorization in Python. Turning categorical variables into quantitative variables. Data Wrangling. Idenifying Correlation. Correlation – Visualization. Case Example – Classification. Exploratory Data Analysis. Usign Logistic and Decision Tree for Model Development. Model Evaluation using Visualization. Model Evaluation. Overfitting, Underfitting and Model Selection.

PLACEMENT GUARANTEE:

We are the no 1 Business Analytics training classes in Mumbai. At Meul Tech , we train students for interviews and offer placement assistance in corporate companies. We will make you capable of creating robust business analytics model using latest technology like python and R.

CERTIFICATION:

At the end of the Business Analytics training course in Mumbai, you will get a Business Analytics from us which will be very helpful for you in your future.

AWARDS:

Meul Tech has been awarded multiple times as the “Most Promising Software Training Institute in Maharashtra” and “Social Impact Award for Outstanding Contribution to Indian Education System” at Education Leadership Awards.

OTHER RELATED COURSES:

Meul Tech provides the best Business Analytics Training in Mumbai. Along with Business Analytics course, you can also learn,

1. Python Training in Mumbai.
2. Machine Learning(Python) Training.

CONTACT US:

If you are looking for a successful career in Business Analytics , we invite you to visit our training facility or contact us: