Data Science Professional Program

  • High Demand In The It Industry: Data science is used by companies to analyze business data, understand customer behavior, make predictions, and support better decision-making.
  • Useful Across Multiple Industries: Data science is used in healthcare, finance, education, e-commerce, marketing, retail, and business intelligence.
  • Build Data-driven Problem-solving Skills: Learners understand how to convert raw data into meaningful insights, visual reports, and prediction-based solutions.
1.5 Months ₹11,999 ₹8,999
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Data Science Professional Program
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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 45 days course helps learners build a strong foundation in Python, statistics, data preprocessing, data analysis, visualization, machine learning basics, and practical data science project development.

Course with Live Project

No Refund Available

data analysis and visualization foundation: learners understand how to clean, analyze, visualize, and interpret datasets using python, pandas, numpy, matplotlib, and seaborn basics.

statistics and machine learning basics: work with basic statistics, probability, correlation, regression concepts, classification basics, and simple predictive modeling.

Practical Data Science Project Development: Develop Projects Like Student Performance Analysis, Sales Data Analysis, Customer Behavior Analysis, And Beginner-level Prediction Models.

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 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
  • • 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
  • • identifying trends and outliers
  • • applying statistics to data science

  • • 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

  • • 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

  • • 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 data cleaning techniques
  • • handling missing and duplicate data
  • • preparing features for model training
  • • understanding feature selection methods
  • • transforming data for better accuracy
  • • working with real-world datasets

  • • 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 basics

  • • planning real-world data projects
  • • cleaning and preparing project data
  • • performing data analysis and visualization
  • • building predictive machine learning models
  • • testing and evaluating model accuracy
  • • presenting final project outcomes

  • • statistical analysis
  • • data visualization with statistics
  • • use dataset of iris and handle missing values
  • • train ml regression model on house price prediction

  • • build ml model on cancer detection
  • • fitness progress prediction system

  • • customer churn prediction
  • • sales forecasting analysis
  • • house price prediction system
  • • employee attrition analysis
  • • e-commerce recommendation insights
  • • student performance prediction

  • • python
  • • numpy
  • • pandas
  • • matplotlib
  • • seaborn
  • • scikit-learn
  • • jupyter notebook / google colab
  • • csv & excel dataset handling

Skills Developed with Data Science Course

Python For Data Science: Learn python fundamentals, data types, conditions, loops, functions, file handling, and basic problem-solving for data tasks.
Statistics Basics: Understand mean, median, mode, variance, standard deviation, probability, correlation, and data distribution concepts.
Data Collection And Cleaning: Work with csv files, missing values, duplicate records, incorrect data, outliers, and basic data preparation techniques.
Numpy And Pandas: Practice arrays, dataframes, filtering, sorting, grouping, merging, transformation, and exploratory data analysis.
Data Visualization: Create line charts, bar charts, scatter plots, histograms, heatmaps, and visual reports using matplotlib and seaborn.
Exploratory Data Analysis: Explore datasets, identify patterns, compare variables, detect trends, and generate useful insights from data.
Machine Learning Basics: Learn supervised learning, regression, classification, model training, testing, and simple prediction workflows.
Feature Understanding: Understand features, labels, target variables, input data, output data, and dataset preparation for models.
Data Science Tools Usage: Work with python, jupyter notebook, google colab, numpy, pandas, matplotlib, seaborn, and scikit-learn basics.
Data Science Project Skills: Practice planning, cleaning data, analyzing datasets, creating visualizations, building simple models, and presenting findings.

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, and preparing basic reports.

Data Analyst Intern:

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

Python Data Assistant:

Use python libraries to clean, process, analyze, and visualize datasets for beginner-level data projects.

Ml Beginner Role:

Build simple prediction models, test model accuracy, and support machine learning project workflows.

Business Data Assistant:

Help teams understand sales, customer, finance, and marketing data through analysis and visual reports.

Why Enroll in Data Science with Solitaire Learning?

Beginner-friendly Data Science Training: The course starts from python, statistics, and data basics, making it suitable for learners starting their data science journey.
Practical Dataset-based Learning: Learners work with real-world datasets and understand data science through hands-on analysis and project work.
Industry-relevant Tools: The course covers python, numpy, pandas, matplotlib, seaborn, scikit-learn basics, jupyter notebook, and google colab.
Mentor-guided Learning: Learners receive mentor support for concept clarity, data cleaning, visualization, coding practice, and project development.
Strong Foundation For Advanced Data Science Courses: The course builds a solid base before moving into 2 months, 3 months, 4 months, or 6 months data science programs.
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.
Course FAQ

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