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.
Frequently Asked Questions

Have Questions About This Course?

Find answers to the most common questions learners ask before enrolling.

Basic Python knowledge is recommended, but beginner-level guidance is also provided during the training program. Students can gradually build programming skills.

Basic statistics, logical thinking, and analytical understanding are helpful for learning Data Science concepts. Advanced mathematics is not compulsory for beginners.

Yes, beginner-friendly batches are available for students from technical as well as non-technical backgrounds. Concepts are explained step-by-step with practical examples.

A laptop with minimum 8GB RAM, i3/i5 processor, and stable internet connection is recommended for smooth coding, visualization, and project work.

No, Machine Learning fundamentals are covered within the course itself. Students start from basic concepts and gradually move toward advanced topics.

Data Science is a field that involves collecting, processing, analyzing, and interpreting data to solve business and real-world problems. It combines programming, statistics, machine learning, and data visualization techniques.

Students learn Python programming, data analysis, machine learning, statistics, data visualization, predictive modeling, and real-world project development. The course focuses on both theoretical and practical implementation.

Yes, students practice on industry-level datasets related to healthcare, business, finance, retail, and analytics problems. This helps learners gain practical exposure to real scenarios.

Yes, Machine Learning is an important part of Data Science training. Students learn predictive modeling, classification, regression, clustering, and model evaluation techniques.

The course includes Python, Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn, and Jupyter Notebook. Students also learn data preprocessing and visualization tools.

Yes, students build practical projects like recommendation systems, prediction models, sentiment analysis systems, and business analytics applications. These projects help create strong portfolios.

Yes, every module contains assignments, coding exercises, and implementation-based practice tasks. Regular practice helps improve analytical and problem-solving skills.

Yes, students learn charts, graphs, dashboards, and storytelling techniques using visualization libraries and analytics tools. Visualization helps present insights more effectively.

Yes, Data Science is one of the most in-demand career fields with opportunities in analytics, AI, Machine Learning, and business intelligence domains. Skilled professionals are highly valued across industries.

Yes, students receive project mentorship, portfolio-building support, and guidance for internships and placements. Trainers help students build industry-oriented projects.
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