Data Science Training Course Complete Program
Gain proficiency in extracting actionable insights from data with a comprehensive data science course. Equip yourself for high-paying careers in analytics, machine learning, and AI. Learn Python, SQL, statistics, ML algorithms, data visualization, and more. Get hands-on experience with real-world projects and prepare for top data science certifications.
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Average annual salary for Data Science professionals
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Industry-Relevant Data Science Training at DIGIT institute
Our data science training course at DIGIT institute® equips you with essential skills in data manipulation, analysis, and modeling. Learn to clean and wrangle data (Pandas), perform statistical analysis (SciPy), visualize insights (Matplotlib/Seaborn), query databases (SQL), and build predictive models (Scikit-learn). Deploy ML models and interpret results for business decisions.
- In-Depth Focus on Data Wrangling, Statistical Analysis, and Visualization
Our professional instructors bring a wealth of real-world experience and knowledge, offering personalized guidance and mentorship throughout the course. With hands-on projects, you will not only learn but also apply your skills in practical scenarios. The program also includes job-oriented training with mock tests and mock interviews.
- Project-Based Learning for Real-World Data Scenarios
Your Data Science Learning Journey
Unlock your potential in data science with DIGIT institute's professionally curated curriculum. Join us and embark on a transformative journey to mastering data analytics tools and techniques.
Python Basics
History of Python, installation using Anaconda, setting up environments, Jupyter Notebook, VSCode configuration.
Variables, data types, operators, type conversions, input/output, scripting vs interactive mode.
Decision making using if/else, nested conditions, loops, practical scenario-based exercises.
Defining functions, default/keyword arguments, recursion, lambda, map/filter/reduce, decorators.
List/dict comprehensions, generators, iterators, context managers.
Classes, objects, constructors, polymorphism, inheritance, abstraction, encapsulation.
Working with CSV, JSON, Excel; using OS, sys modules; reading/writing files; exception handling.
Python Basics
History of Python, installation using Anaconda, setting up environments, Jupyter Notebook, VSCode configuration.
Variables, data types, operators, type conversions, input/output, scripting vs interactive mode.
Decision making using if/else, nested conditions, loops, practical scenario-based exercises.
Defining functions, default/keyword arguments, recursion, lambda, map/filter/reduce, decorators.
List/dict comprehensions, generators, iterators, context managers.
Classes, objects, constructors, polymorphism, inheritance, abstraction, encapsulation.
Working with CSV, JSON, Excel; using OS, sys modules; reading/writing files; exception handling.
Statistics & Maths
Mean, median, mode, variance, skewness, kurtosis, exploratory numerical summaries.
Random variables, PMF, PDF, CDF, Bernoulli, Binomial, Normal, Poisson distributions.
Hypothesis testing, p-values, Z-test, T-test, Chi-square test, ANOVA, confidence intervals.
Vectors, matrices, dot products, eigenvalues, eigenvectors, decomposition techniques (SVD).
Derivatives, chain rule, gradients, optimization concepts, cost minimization.
Statistics & Maths
Mean, median, mode, variance, skewness, kurtosis, exploratory numerical summaries.
Random variables, PMF, PDF, CDF, Bernoulli, Binomial, Normal, Poisson distributions.
Hypothesis testing, p-values, Z-test, T-test, Chi-square test, ANOVA, confidence intervals.
Vectors, matrices, dot products, eigenvalues, eigenvectors, decomposition techniques (SVD).
Derivatives, chain rule, gradients, optimization concepts, cost minimization.
ML & DL
( Machine Learning & Deep Learning )Machine Learning
Types of ML, bias-variance, data splitting, cross-validation, data leakage, pipeline basics.
Linear, polynomial, Ridge, Lasso, ElasticNet; evaluation metrics such as RMSE, MAE, R2.
Logistic regression, KNN, SVM, Naive Bayes, decision trees, random forests, gradient boosting, XGBoost.
K-Means, Hierarchical clustering, PCA, t-SNE, anomaly detection techniques.
Scaling, encoding, feature selection, GridSearchCV, RandomSearchCV, pipelines.
Deep Learning
Neurons, activation functions, loss functions, optimizers (SGD, Adam), backpropagation.
Building networks, dropout, batch normalization, regularization, tuning hyperparameters.
Convolution, pooling, filters, pretrained models like VGG, ResNet, MobileNet.
ML & DL
( Machine Learning & Deep Learning )Machine Learning
Types of ML, bias-variance, data splitting, cross-validation, data leakage, pipeline basics.
Linear, polynomial, Ridge, Lasso, ElasticNet; evaluation metrics such as RMSE, MAE, R2.
Logistic regression, KNN, SVM, Naive Bayes, decision trees, random forests, gradient boosting, XGBoost.
K-Means, Hierarchical clustering, PCA, t-SNE, anomaly detection techniques.
Scaling, encoding, feature selection, GridSearchCV, RandomSearchCV, pipelines.
Deep Learning
Neurons, activation functions, loss functions, optimizers (SGD, Adam), backpropagation.
Building networks, dropout, batch normalization, regularization, tuning hyperparameters.
Convolution, pooling, filters, pretrained models like VGG, ResNet, MobileNet.
NLP
( Natural Language Processing )Tokenization, stemming, lemmatization, stopword removal, normalization, POS tagging.
TF-IDF, Bag-of-Words, n-grams, Word2Vec, GloVe embeddings.
Sentiment analysis, spam classification, topic modeling using LDA.
NLP
( Natural Language Processing )Tokenization, stemming, lemmatization, stopword removal, normalization, POS tagging.
TF-IDF, Bag-of-Words, n-grams, Word2Vec, GloVe embeddings.
Sentiment analysis, spam classification, topic modeling using LDA.
Big Data & Spark
RDDs, DataFrames, Spark SQL, transformations and actions.
Building scalable ML models, pipeline creation, working with large datasets.
Big Data & Spark
RDDs, DataFrames, Spark SQL, transformations and actions.
Building scalable ML models, pipeline creation, working with large datasets.
More Concepts
( Data Science )Data Analysis Tools
Arrays, slicing, indexing, broadcasting, vectorization, random sampling, matrix operations.
DataFrames, merging, joining, grouping, aggregation, pivot tables, time-series data, window functions.
Handling missing values, duplicates, outliers, encoding techniques, datatype conversions.
Data Visualization
Plotting basics, customizing charts, multi-plot layouts, styling plots.
Distribution plots, categorical plots, statistical visualizations, heatmaps, pairplots.
Interactive graphs, dashboards, map plots, animations, real-time visualization.
Capstone Projects
Data cleaning, EDA, ML model building, optimization, saving and loading model.
Text classification pipeline using TF-IDF or embeddings.
Customer segmentation using PySpark and MLlib.
More Concepts
( Data Science )Data Analysis Tools
Arrays, slicing, indexing, broadcasting, vectorization, random sampling, matrix operations.
DataFrames, merging, joining, grouping, aggregation, pivot tables, time-series data, window functions.
Handling missing values, duplicates, outliers, encoding techniques, datatype conversions.
Data Visualization
Plotting basics, customizing charts, multi-plot layouts, styling plots.
Distribution plots, categorical plots, statistical visualizations, heatmaps, pairplots.
Interactive graphs, dashboards, map plots, animations, real-time visualization.
Capstone Projects
Data cleaning, EDA, ML model building, optimization, saving and loading model.
Text classification pipeline using TF-IDF or embeddings.
Customer segmentation using PySpark and MLlib.
Exploring Course Essentials
Embark on a journey to understand the critical elements of our course.
Learn Online
Embark on a learning journey with our diverse courses, available online.
professional Instructors
Begin your learning journey with our accomplished instructors, professionals in their fields.
1:1 Mentorship
Experience personalized growth with our 1:1 mentorship, where dedicated mentors guide you toward success.
Low Student - Teacher Ratio
Benefit from personalized guidance and a supportive learning environment with our small class sizes.
Flexible Learning Options
Enhance your education with flexible choices that fit your schedule and preferences.
Hands-On Projects
Engage in practical, real-world projects to reinforce key skills and essential concepts.
Comprehensive Curriculum
Follow a meticulously designed curriculum that takes you from core fundamentals through advanced, real‑world applications.
Regular Assessments
Keep your learning on track with periodic quizzes and projects that reinforce concepts and measure your progress.
Career Support
Get expert career advice, resume building, and interview prep to launch your dream job.
Track Your Progress Toward Enrollment
1. Registration
Register online, provide details, help admissions understand your career goals.
2. Test
Submit application, then an assessment ensures eligibility and program aptitude.
3. Offer Applicable
After review, eligible candidates get scholarship offers and confirmation email.
4. Fee Payment
Review your admission offer, promptly pay fee to confirm enrollment.
Advance Your Career with Our Data Science Certification
Industry Recognition
Our certifications are well-regarded in the software industry, providing valuable opportunities for career advancement
Skill Validation
Our certifications validate your skills through practical application, equipping you for the workforce and demonstrating your expertise to employers.
Career Advancement
Achieve new heights in your professional journey with our certifications.
Shared Experiences from Our Students
Join Our Upcoming Batches
Secure your spot in the next batch. Limited seats available. Enroll now and start your learning journey with expert-led live training.
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