Data Science Expertise

  • High Demand In The It Industry: Data science is used by companies to analyze data, predict outcomes, understand customers, detect risks, and support better business decisions.
  • Useful Across Multiple Industries: Data science is used in healthcare, finance, education, e-commerce, marketing, retail, business intelligence, and technology companies.
  • Build Strong Problem-solving Skills: Learners understand how to convert raw data into insights, reports, visualizations, and predictive models for real-world problems.
3 Months ₹22,999 ₹16,999
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Data Science Expertise
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Course Overview

Data Science is the process of collecting, cleaning, analyzing, visualizing, and modeling data to solve real-world problems and make predictions. This 3 months course helps learners build strong Data Science skills using Python, statistics, data preprocessing, exploratory data analysis, data visualization, machine learning, feature engineering, model evaluation, and real-world data science projects.

Course with Live Project

No Refund Available

End-to-end Data Science Workflow: Learners Understand The Complete Workflow From Data Collection And Cleaning To Analysis, Visualization, Machine Learning, Evaluation, And Project Presentation.

Machine Learning And Predictive Modeling: Work With Regression, Classification, Clustering, Feature Engineering, Model Evaluation, And Prediction-based Solutions Using Python.

Portfolio-based Data Science Projects: Develop Practical Projects Like Social Media Sentiment Analysis, Sales Forecasting Models, Customer Behavior Analysis, And Business Prediction Systems.

Course Content

  • • Understanding data science fundamentals
  • • Exploring real-world data applications
  • • Learning data science lifecycle
  • • Understanding roles in data science
  • • Comparing ai, ml, and data science
  • • Exploring business problem solving
  • • Setting up data science environment
  • • Learning data science career paths

  • • Learning python programming fundamentals
  • • Understanding variables and data types
  • • Working with conditional statements logic
  • • Using loops for data processing
  • • Creating functions and reusable modules
  • • Managing data with python collections
  • • Understanding file handling concepts
  • • Handling errors and exceptions
  • • Exploring python libraries for data science

  • • Understanding mean, median, and mode
  • • Learning variance and standard deviation
  • • Exploring probability and predictions
  • • Understanding correlation between variables
  • • Learning linear algebra fundamentals
  • • Understanding vectors and matrices
  • • Exploring data distribution patterns
  • • Applying statistics to data science
  • • Understanding optimization techniques basics

  • • Understanding numpy for data operations
  • • Working with arrays and calculations
  • • Learning pandas for data analysis
  • • Managing data using dataframes
  • • Reading csv and excel files
  • • Cleaning and preparing datasets
  • • Handling missing data efficiently
  • • Transforming data for better analysis
  • • Performing exploratory data analysis
  • • Generating insights from datasets

  • • Understanding data visualization concepts
  • • Working with matplotlib library
  • • Creating seaborn statistical visualizations
  • • Building charts for data analysis
  • • Understanding correlation heatmaps
  • • Creating interactive data visualizations
  • • Presenting insights through storytelling
  • • Designing professional data reports

  • • Understanding machine learning concepts
  • • Learning types of machine learning
  • • Working with supervised learning models
  • • Understanding regression algorithm concepts
  • • Exploring classification algorithm basics
  • • Learning unsupervised learning techniques
  • • Working with clustering algorithms
  • • Measuring model accuracy performance
  • • Training models using real datasets
  • • Understanding model evaluation concepts

  • • Understanding feature selection techniques
  • • Learning feature engineering concepts
  • • Handling missing and duplicate data
  • • Preparing features for model training
  • • Understanding data scaling methods
  • • Transforming data for better accuracy
  • • Improving dataset quality efficiently

  • • Understanding predictive analytics concepts
  • • Measuring accuracy and performance metrics
  • • Understanding confusion matrix concepts
  • • Learning precision and recall metrics
  • • Understanding overfitting and underfitting
  • • Exploring cross validation techniques
  • • Performing model performance optimization

  • • Understanding deep learning concepts
  • • Learning artificial neural networks
  • • Understanding activation function basics
  • • Exploring hidden layer concepts
  • • Working with tensorflow basics
  • • Understanding neural network applications

  • • Understanding end-to-end data workflow
  • • Solving real-world business problems
  • • Analyzing customer behavior patterns
  • • Building data-driven decision systems
  • • Creating business insight reports
  • • Understanding predictive analytics applications

  • • Building industry-level data projects
  • • Solving real-world data problems
  • • Practicing data science interview questions
  • • Preparing professional project documentation
  • • Building portfolio ready data projects
  • • Presenting final project outcomes

  • • Machine learning model training
  • • Data preprocessing pipeline task
  • • Predictive modeling exercise
  • • Eda report development
  • • Statistical interpretation exercise

  • • Social media sentiment analyzer
  • • Sales forecasting prediction model

Skills Developed with Data Science Course

Python For Data Science: Learn python programming, functions, modules, file handling, oop basics, data structures, and coding logic for data tasks.
Statistics And Mathematics: Understand mean, median, mode, variance, standard deviation, probability, correlation, covariance, linear algebra basics, and distributions.
Data Cleaning And Preprocessing: Work with missing values, duplicate records, incorrect data, outliers, encoding, scaling, normalization, and dataset preparation.
Numpy And Pandas: Practice arrays, dataframes, csv handling, filtering, sorting, grouping, merging, transformation, and exploratory data analysis.
Data Visualization: Create charts, graphs, scatter plots, histograms, heatmaps, trend visuals, and insight-based reports using matplotlib and seaborn.
Exploratory Data Analysis: Explore datasets, identify patterns, compare variables, detect trends, find relationships, and generate useful business insights.
Machine Learning: Learn supervised learning, unsupervised learning, regression, classification, clustering, model training, testing, and evaluation.
Feature Engineering: Practice feature selection, feature creation, feature transformation, encoding, scaling, and improving dataset quality for models.
Model Evaluation: Learn train-test split, accuracy, confusion matrix, precision, recall, f1-score, overfitting, underfitting, and performance comparison.
Data Science Project Skills: Practice planning, cleaning data, analyzing datasets, building models, documenting workflow, and presenting intermediate-level projects.

Career Opportunities after Data Science Course

This course opens doors to multiple high-demand career paths across industries.

Data Science Intern:

Support data projects by cleaning datasets, analyzing data, creating charts, building models, and preparing project reports.

Data Analyst Intern:

Work on python-based data analysis, visualization, reporting, business insight generation, and dashboard support tasks.

Machine Learning Intern:

Build simple ml models, test accuracy, compare model performance, and support prediction-based project workflows.

Python Data Assistant:

Use python libraries to clean, process, analyze, visualize, and prepare datasets for data science tasks.

Predictive Modeling Assistant:

Support forecasting, customer behavior prediction, risk analysis, trend analysis, and business prediction tasks

Why Enroll in Data Science with Solitaire Learning?

Beginner-to-intermediate Curriculum: The course starts from python, statistics, and data basics, then moves toward machine learning, feature engineering, and projects.
Practical Dataset-based Learning: Learners work with real-world datasets and understand data science through hands-on analysis, visualization, and model building.
Industry-relevant Tools: The course covers python, numpy, pandas, matplotlib, seaborn, scikit-learn, jupyter notebook, and google colab.
Mentor-guided Project Support: Learners receive mentor guidance for concept clarity, data cleaning, visualization, coding practice, model building, and portfolio preparation.
Strong Foundation For Advanced Data Science: The course prepares learners for 4 months and 6 months advanced data science programs with deeper ml, deployment, and industry-level projects.

Ready to Take the Next Step in Your Career?

Join our expert-led training program, gain industry-recognized skills, and move closer to your professional goals. Seats are limited — enroll today!

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