Data Science & Machine Learning Course in Udaipur — Python, Power BI, Tableau & Advanced Excel

Keen Infotech's Data Science & Machine Learning course in Udaipur is a ~200-hour, 5–6 month program covering Python, NumPy, Pandas, statistics, supervised and unsupervised machine learning, Power BI, Tableau, and Advanced Excel. It is delivered in classroom and online mode from an ISO 9001:2015 certified institute, and includes real-world projects, a capstone, and placement assistance.

• ~200 hours across 15 modules (5–6 months)

• Classroom (Udaipur) & Online batches

• Python + Power BI + Tableau + Advanced Excel in one course

• Hands-on projects and a final capstone

• ISO 9001:2015 certified, 16+ years training experience

Hours are indicative — actual pace depends on batch schedule (live vs recorded sessions).

• Introduction to Python, installation (Anaconda, Jupyter, VS Code)

• Variables, data types, type casting, operators

• Conditional statements & loops (for, while)

• String manipulation & string methods

• Functions: arguments, default/keyword args, lambda functions

• List, tuple, dictionary, set — comprehensions

• OOP: classes, objects, inheritance, polymorphism, encapsulation

• Exception handling: try-except-finally, custom exceptions

• File handling: read/write text, CSV, JSON files

• Modules, packages, pip, virtual environments

• NumPy arrays: creation, indexing, slicing, reshaping

• Array operations, broadcasting, vectorization

• Pandas Series & DataFrame creation

• Data import: CSV, Excel, JSON, SQL databases, APIs

• Data inspection: head, info, describe, dtypes

• Data cleaning: handling NaN, duplicates, wrong data types

• Data transformation: apply, map, applymap, lambda

• Merging, joining, concatenating datasets

• GroupBy operations & aggregation

• Pivot tables & cross-tabulation in Pandas

• Date-time handling & time series basics

• Working with large datasets (chunking, optimization)

• Matplotlib: figure, axes, subplots, customization

• Line, bar, pie, scatter, histogram, box plots

• Seaborn: distplot, countplot, heatmap, pairplot, violin plot

• Correlation heatmaps for feature analysis

• Plotly & Cufflinks: interactive charts

• Dashboard-style visual storytelling

• Choosing the right chart for the right data

• Population vs sample, sampling techniques

• Measures of central tendency & dispersion

• Skewness, kurtosis, outlier detection (Z-score, IQR)

• Probability basics, conditional probability

• Distributions: Normal, Binomial, Poisson, Uniform

• Central Limit Theorem

• Hypothesis testing: Z-test, T-test, Chi-square test, ANOVA

• Confidence intervals & p-value interpretation

• Correlation vs causation

• Covariance & correlation coefficient

• ML workflow: data prep → train/test split → model → evaluate

• Simple & Multiple Linear Regression

• Polynomial Regression

• Regularization: Ridge, Lasso, ElasticNet

• Logistic Regression for classification

• Decision Trees: entropy, Gini index, pruning

• Random Forest & feature importance

• K-Nearest Neighbors (KNN)

• Support Vector Machines (SVM)

• Naive Bayes classifier

• Model evaluation: MAE, MSE, RMSE, R², accuracy, precision, recall, F1, ROC-AUC

• Confusion matrix interpretation

• K-Means clustering & elbow method

• Hierarchical clustering & dendrograms

• DBSCAN (density-based) clustering

• Principal Component Analysis (PCA) — dimensionality reduction

• Association rule mining (Apriori, Market Basket Analysis)

• Anomaly / outlier detection techniques

• Handling categorical data: Label Encoding, One-Hot Encoding

• Feature scaling: Normalization, Standardization

• Feature selection techniques (correlation, chi-square, RFE)

• Handling imbalanced datasets (SMOTE, undersampling)

• Cross-validation (K-Fold, Stratified K-Fold)

• Hyperparameter tuning: GridSearchCV, RandomizedSearchCV

• Bias-variance tradeoff, overfitting/underfitting

• Bagging vs Boosting concepts

• Random Forest (recap), AdaBoost, Gradient Boosting

• XGBoost, LightGBM basics

• Introduction to Neural Networks: perceptron, activation functions

• TensorFlow/Keras basics: building a simple ANN

• Intro to CNN (image data) — conceptual overview

• Intro to NLP: text preprocessing, tokenization, sentiment analysis basics

• Saving/loading models (Pickle, Joblib)

• Building a simple web app with Streamlit

• Building REST API with Flask for ML models

• Basics of deploying on cloud (Heroku/Render, overview)

• Formulas: VLOOKUP, HLOOKUP, XLOOKUP, INDEX-MATCH

• Logical functions: nested IF, IFS, AND/OR

• Text functions: LEFT, RIGHT, MID, CONCATENATE, TEXT

• Date & time functions

• Data validation, conditional formatting

• PivotTables & PivotCharts (advanced grouping, slicers)

• Power Query: data import, transformation, merging queries

• Power Pivot & data modeling

• DAX basics: calculated columns & measures

• What-if analysis: Goal Seek, Data Tables, Scenario Manager

• Dashboard building using charts + slicers + form controls

• Power BI ecosystem: Desktop, Service, Mobile

• Connecting to data sources (Excel, SQL, Web, APIs)

• Data transformation with Power Query Editor

• Data modeling: relationships, star schema

• DAX formulas: calculated columns, measures, time intelligence functions

• Visualizations: bar, line, map, matrix, card visuals

• Slicers, filters, drill-through, bookmarks

• Interactive dashboard design

• Publishing reports to Power BI Service

• Sharing, row-level security, scheduled data refresh

• Power BI Mobile app basics

• Tableau Desktop interface & navigation

• Connecting to data sources

• Dimensions vs Measures, discrete vs continuous

• Building charts: bar, line, scatter, maps, treemaps

• Calculated fields, table calculations, LOD expressions

• Filters, parameters, sets, groups

• Dashboard actions: filter, highlight, URL actions

• Story points for data storytelling

• Publishing to Tableau Public / Tableau Server

• Tableau vs Power BI — when to use which

• SQL basics: SELECT, WHERE, ORDER BY, GROUP BY

• Joins: INNER, LEFT, RIGHT, FULL

• Subqueries & CTEs

• Aggregate functions

• Connecting Python / Power BI / Tableau to SQL databases

• Exploratory Data Analysis (EDA) project on a real dataset

• Sales/Retail data analysis with Python

• Customer churn prediction (classification project)

• House price prediction (regression project)

• Customer segmentation (clustering project)

• Power BI sales dashboard project

• Tableau HR/Finance dashboard project

• Excel-based business MIS report project

• Capstone: end-to-end project — data collection → cleaning → analysis → ML model → BI dashboard presentation

• Resume & LinkedIn profile building

• Portfolio building (GitHub + Kaggle)

• Mock interviews (technical + HR)

• Case study / business problem-solving practice

• Certification on course completion

• Placement assistance / job referrals

Python has become one of the most in-demand programming languages due to its versatility, ease of use, and vast ecosystem of libraries. Here's why Python could be your best career choice:

• Easy to Learn, Write, and Use

• High Demand in the Job Market

• Versatile Across Multiple Domains

• Extensive Libraries and Frameworks

• Ideal for AI and Machine Learning

• Excellent for Rapid Prototyping

• Massive Community Support

• Future-Proof Language

• Open-Source and Cost-Effective

• Cross-Platform and Extensible

• Exploratory Data Analysis (EDA) on a real dataset

• Sales/Retail data analysis with Python

• Customer churn prediction (classification)

• House price prediction (regression)

• Customer segmentation (clustering)

• Power BI sales dashboard

• Tableau HR/Finance dashboard

• Excel-based business MIS report

• Capstone: end-to-end project — data collection → cleaning → analysis → ML model → BI dashboard presentation