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Key Highlights

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Comprehensive curriculum
Comprehensive coverage of Python, R, and SQL for data manipulation and analysis.
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Practical learning
Real-world projects that provide hands-on experience and practical application.
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Industry expertise
Sessions led by industry experts offering insights into current trends and practices.
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Career-focused training
Career support, including guidance and job placement assistance, to help you succeed.
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Data manipulate
Specializations in AI and ML to deepen your knowledge in these cutting-edge fields.
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Flexible learning
Access to a dedicated learning platform, allowing flexible and continuous learning.

Things To Know

Eligibility Criteria ?
  • Basic programming knowledge is recommended, but the course is accessible to beginners as it starts with foundational topics.
The program lasts 6 months, with part-time study options to fit your schedule.
  • Graduates can pursue careers as Data Scientists, ML Engineers, AI Specialists, and Data Analysts. with strong skills in data-driven problem-solving and innovation.
If you are ready to learn and want to light up your future with us. Enroll now in this course.
Apply Now

About the Course

data analysis
Industry-Relevant Skills

Focus on acquiring skills that are in high demand across various sectors. This course is designed with input from industry professionals to ensure it meets current market needs.

Practical focus
Hands-On Learning

Emphasize practical, hands-on learning through projects and case studies that simulate real-world problems, allowing you to apply theoretical knowledge in practical scenarios.

Communicate effectively
Cutting-Edge Technologies

Stay ahead of the curve by learning the latest tools and techniques in AI and ML, positioning yourself as a leader in the field.

Suitable levels
Expert Mentorship

Benefit from the guidance of seasoned professionals who bring real-world experience to the classroom, helping you bridge the gap between theory and practice.

data visualization
Holistic Learning Experience

Engage in an interactive learning environment that combines video lectures, live sessions, peer discussions, and one-on-one mentoring.

Career prospects
Strong Placement Support

Leverage our extensive network of industry connections and strong placement support to secure roles in leading organizations, ensuring that your education translates into a successful career.

Content

Data Science with Python, Artificial Intelligence and Machine Learning, is an intensive 240-hour course, focused on equipping participants with practical skills in Python, AI & ML. The course includes hands-on projects, ethical considerations and communication training, preparing Graduates for roles such as Data Scientist or Machine Learning Engineer

  • Introductions to Data Science
  • Domains in Data Science
  • Need of Data Science
  • Use of Data Science in Business
  • Lifecycle of Data Science Projects
  • Data Science Tools and Technologies
  • Basics of Excel for Analysis
  • Required Skill for Data Science
  • Types of data
  • Descriptive vs Inferential Statistics
  • Sampling Techniques
  • Measures of Central Tendency and Dispersion
  • Hypothesis & Inferences Testing
      • 1 . F Test
      2 . T Test
      3 . ANNOVA
      4 . Chi Square Test
  • Confidence Interval
  • Central Limit Theorem
  • P value
  • Variables
  • Co relation and Co Variance

Excel Essentials

  • Excel Essentials
  • Working with Multiple Worksheet
  • Cell Referencing
  • Working with Data Lists
  • Conditional Formatting
  • Data Validation
  • What-If Analysis
  • Formula Auditing
  • Protection

Formulas & Functions

  • Conditional Function
  • Text & Statistical Function
  • Financial Function
  • Creating HLOOKUP and VLOOKUP Functions
  • Advanced Conditional Formatting
  • Advanced Lookup and Reference Functions

Dashboard Designing (MIS)

  • Creating Dashboard
  • Charts & Sparkline’s
  • Use of Power Pivots for Data Analysis
  • Use of Power Queries & Power Map
  • Pivot table Dashboard with Slicers

Automation

  • Working with External Data
  • Exporting & Importing Data
  • Excel, Access, PPT, TXT, CSV
  • Updating Charts, Table in PowerPoint
  • Converting Reports into PDF
  • Exporting Charts, Tables to PowerPoint
  • Introduction to Python
  • Command line basics
  • Numbers, Operators & Comments
  • Variables & Strings
  • Boolean & Conditional Logic
  • Looping in Python
  • Lists
  • Dictionaries

Visualisation with Seaborn

  • Introduction to Seaborn
  • Seaborn Installation
  • Basics of Plotting
  • Plots Generation
  • Visualising the Distribution of a Dataset
  • Selection of color palettes
  • Lists
  • Dictionaries
  • Tuples & Sets
  • Functions
  • Modules
  • OOPS
  • File I/O
  • Handling Missing values(Numerical / Categorical)
  • Graphical Exploratory Analysis (Seaborn / Matplotlib)

Visualisation with Matplotlib

  • Matplotlib Installation
  • Matplotlib Basic Plots & it's Containers
  • Matplotlib components and it’s properties
  • Py lab & Py plot
  • Scatter plots
  • 2D Plots
  • Histograms
  • Bar Graphs
  • Pie Charts
  • Box Plots
  • Introduction To AI
  • Why AI is Required
  • What is Neuron
  • Architecture of Artificial Neural Network
  • Neural Network Modules
  • Activation Functions
  • Optimization Function
  • Cost function
  • Dense Neural Network
  • Regularization
  • Gradient Descent

Image Classification

  • Basic Intro to CNN
  • CNN (Convolution Neural Network)
  • CNN Architecture Building
  • Transfer Learning (VGG16 / VGG 19 / RESNET 50 / Inception V3)

ANN (Artificial Neural Network)

  • Simple ANN Model

NLP (Natural Language Processing)

  • Basic Intro to NLP
  • Simple NLTK (stemming, lemmatization, regex, stop words, corpus, unigram, bigram, trigram)
  • BAG for words (count vectorization)
  • TD-IDF-term frequency inverse document frequency

  • Word embedding:

    • GloVe
    • Word2Vec
    • FastText
    • Keyed Vector
    • TextBlob

SUPERVISED LEARNING

  • Linear Regression / Multi-Linear Regression
  • Logistic Regression
  • Decision Tree (CART)
  • Ensemble Learning
  • Random Forest
  • xgBoost
  • K Nearest Neighbors (KNN)
  • Support Vector Machine (SVM)
  • Naive Bayes Classifier (NBC)
  • Grid Search CV and Random Search CV
  • Linear Discriminant Analysis (LDA)

UNSUPERVISED

  • Hierachical Clustering / Dendograms
  • K Means Clustering
  • DBSCAN
  • MINI BATCH K MEANS
  • DBSCAN
  • MINI BATCH K MEANS

METRICS

  • MAE / MSE/ RMSE / R2 and Adjusted R2
  • AUC ROC CURVE / Precision / Recall / F1 score / Confusion Metrics

DIMENSION REDUCTION MODELS

  • PCA
  • Kernal PCA

TIME SERIES ANALYSIS

  • ARIMA
  • FB PROPHET

HYPERPARAMETER TUNING / ADVANCED ML MODELS

  • Overfitting and underfitting
  • Cross Validation
  • Log Loss
  • Elastic net
  • Lasso And Ridge Regression
  • SMOTE
  • SKLEARN Using HyperParameter
  • Model Evalution
  • Gradient Descent

GIT: Complete Overview

  • Introduction to Git & Distributed
  • Version Control
  • Life Cycle
  • Create clone & commit Operations
  • Push & Update Operations
  • Stash, Move, Rename & Delete
  • Operations
  • Selecting & Retrieving Data With SQL
  • Filtering, Sorting, and Calculating Data with SQL
  • Subqueries and Joins in SQL
  • Modifying and Analysing Data with SQ
  • Architecture of Tableau
  • Product Components
  • Working with Metadata and Data Blending
  • Data Connectors
  • Data Model
  • File Types
  • Dimensions & Measures
  • Data Source Filters
  • Creation of Sets
  • Gantt Chart
  • Funnel Chart
  • Waterfall Chart
  • Working with Filters
  • Organising Data and Visual Analytics
  • Working with Mapping
  • Working with Calculations and Expressions
  • Working with Parameters
  • Creating Charts and Graphs
  • Dashboard Creation

Tableau

  • Architecture of Tableau
  • Product Components
  • Working with Metadata and Data Blending
  • Data Connectors
  • Data Model
  • File Types
  • Dimensions & Measures
  • Data Source Filters
  • Creation of sets
  • Funnel Chart
  • Gantt Chart
  • Waterfall Chart
  • Working with Filters
  • Organizing Data and Visual Analytics
  • Working with Mapping
  • Working with Calculations and Expressions
  • Working with Parameters
  • Charts and Graphs
  • Dashboard Creation