Data Science
Foundation Training
Eduhubspot’s Data Science Foundation Training Program takes you from Python fundamentals to advanced machine learning and deep learning concepts. Upon the completion of the course, participants will be able to analyze data, build predictive models, and create intelligent solutions using real-world datasets. This course blends programming, analytics, and AI into one complete learning experience.
A practice-focused data science program where you learn moving from Python coding and data analysis to machine learning models and deep learning concepts. Learn to work with real datasets, automate data workflows, and generate insights instantly, preparing you for the next era of AI-driven decision making.
Why Choose
Data Science Foundation Training
Learning Data Science provides significant benefits, including improved career opportunities and increased job performance. It leads to greater efficiency through automation, more effective data analysis and decision-making, and boosts professional credibility and earning potential.
Improved Career Opportunities
Automation & Efficiency
Better Decision-Making
Who Should Attend the
Data Science Foundation Training
Designed for beginners and professionals who want to build strong foundations in Python, analytics, machine learning, and deep learning with real-world datasets.
Data Science Foundation Training
Roadmap
A practice-focused data science program where you learn moving from Python coding and data analysis to machine learning models and deep learning concepts. The course modules are as follows:
Overview Of Python: Introduction, Installation, Development Environments (Jupyter, VS Code).
Python Basics: Variables, Data Types, Input Output Operations, Syntax.
Operators And Control Flow: Arithmetic, Logical Operators, If-Else, Loops.
Functions And Modules: Function Creation, Reusability, Code Organization.
Data Structures: Lists, Tuples, Sets, Dictionaries For Data Handling.
NumPy Basics: Arrays, Operations, Vectorization, Numerical Computing.
Pandas Fundamentals: Series, DataFrames, Data Manipulation Techniques.
Data Cleaning: Handling Missing Values, Duplicates, and Data Type Conversion.
Data Transformation: Filtering, Sorting, Grouping, Aggregation Operations.
File Handling: Working With CSV, Excel, JSON Data Sources.
Visualization Basics: Matplotlib, Seaborn, Plotting Techniques.
Chart Types: Line, Bar, Histogram, Scatter, Box Plots.
Exploratory Data Analysis: Pattern Identification, Trend Analysis.
Statistical Summary: Mean, Median, Standard Deviation, Distribution Insights.
Data Storytelling: Presenting Insights Using Visualizations.
Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation.
Probability Basics: Distributions, Random Variables, Statistical Concepts.
Correlation Analysis: Covariance, Correlation, Relationship Between Variables.
Hypothesis Testing: Confidence Intervals, Significance Testing Methods.
Data Preparation: Feature Scaling, Encoding, and Data Splitting Techniques.
Machine Learning Basics: Types, Applications, Real World Use Cases.
Learning Types: Supervised, Unsupervised, Reinforcement Learning Concepts.
ML Workflow: Data Preparation, Training, Testing, Evaluation Process.
Scikit-learn Basics: Model Building, Training, and Prediction Workflow.
Model Fundamentals: Features, Labels, Training Data, Testing Data.
Regression Models: Linear Regression, Multiple Regression Concepts.
Classification Models: Logistic Regression, KNN Algorithm Basics.
Tree Models: Decision Trees, Random Forest Techniques.
Model Evaluation: Accuracy, Precision, Recall, F1 Score Metrics.
Model Challenges: Overfitting, Underfitting, Bias, Variance Concepts.
Clustering Basics: K-Means Clustering, Distance Metrics Concepts.
Hierarchical Clustering: Agglomerative Methods, Dendrogram Visualization.
Dimensionality Reduction: PCA Concepts, Feature Reduction Techniques.
Feature Engineering: Feature Selection, Transformation Methods.
Use Cases: Customer Segmentation, Pattern Discovery Applications.
Cross Validation: K-Fold Validation, Model Reliability Techniques.
Hyperparameter Tuning: Grid Search, Random Search Methods.
Performance Improvement: Model Selection, Error Reduction Techniques.
Pipelines: Workflow Automation, Model Building Process Optimization.
Deployment Basics: Introduction To Model Deployment Concepts.
Deep Learning Basics: Neural Networks, AI Concepts, Use Cases.
Neural Networks: Perceptron, Multi-layer Network Structure.
Activation Functions: ReLU, Sigmoid, Tanh Function Concepts.
Training Process: Forward Pass, Backpropagation Basics.
Loss Functions: Error Calculation, Optimization Objectives.
Framework Basics: TensorFlow, Keras Introduction, Model Setup.
Model Building: Layers, Neurons, Network Architecture Design.
Model Training: Epochs, Batch Size, Training Process.
Model Evaluation: Accuracy, Loss, Performance Metrics.
Real Datasets: Training Models On Practical Data.
CNN Basics: Convolutional Neural Networks, Image Processing Concepts.
Image Applications: Classification, Object Detection Basics.
RNN Basics: Recurrent Neural Networks, Sequential Data Handling.
Time Series: Sequence Modeling, Forecasting Techniques.
Real Use Cases: NLP, Vision, AI-Driven Applications.
Problem Definition: Business Understanding, Objective Setting.
Data Collection: Data Sources, Data Gathering Techniques.
Data Preparation: Cleaning, Transformation, Feature Engineering.
Model Lifecycle: Selection, Training, Evaluation Process.
Insights And Deployment: Reporting Results, Real World Applications.
Fast Filling Schedule
Loading...
Certification &
Career Path
What will you get?
A customised certificate from EduHubSpot on successful completion of the Data Science Foundation Training Program.
.webp)
Top Benefits of Learning
Data Science Skills
Success Stories That Speak For
Themselves
Frequently Asked
Questions
Get answers to the most common questions about the Data Science Foundation Training Program.
Yes, the program starts with Python fundamentals and gradually moves to advanced topics required for data analysis, machine learning, and deep learning.
You will work with industry-standard tools such as NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and basic deep learning frameworks.
Yes, you will build models using techniques like regression, classification, and clustering on real-world datasets.
Yes, a strong focus is given to data cleaning, transformation, and preprocessing, which are critical steps before building any model.
Yes, you will learn key evaluation techniques such as accuracy, precision, recall, and model validation methods.
The course introduces neural networks, basic architectures, and how deep learning models are used in real-world applications.
Yes, the course is designed to start from basics and gradually build your understanding, making it suitable even if you do not have a technical background.
Yes, the training includes hands-on practice using real datasets to help you understand practical applications of data science.
Yes, you will learn the full workflow, including data collection, preprocessing, model building, evaluation, and interpretation of results.
Yes, the program is designed to build strong foundational skills required for entry-level roles in data science, analytics, and AI-related fields.