Industry Ready Data Science Program

  • High Demand In The It Industry: Data science is used by companies to analyze data, predict outcomes, understand customers, improve decisions, and build intelligent business solutions.
  • Useful Across Multiple Industries: Data science is used in healthcare, finance, education, e-commerce, marketing, retail, business intelligence, and technology companies.
  • Build Data-driven Problem-solving Skills: Learners understand how to convert raw data into meaningful insights, visual reports, and prediction-based solutions.
2 Months ₹18,999 ₹14,999
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Industry Ready Data Science 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 2 months course helps learners build practical Data Science skills using Python, statistics, data preprocessing, exploratory data analysis, data visualization, machine learning basics, feature understanding, and real-world data science project development.

Course with Live Project

No Refund Available

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

Python, Statistics, And Ml Basics: Work With Python, Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn Basics, Statistics, And Beginner-level Machine Learning Concepts.

Practical Data Science Project Development: Develop Projects Like Movie Recommendation Systems, Customer Personality Analysis, Sales Analysis, Student Performance Analysis, And Basic 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 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
  • • 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
  • • 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 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
  • • Improving dataset quality efficiently

  • • Understanding training and testing data
  • • Measuring accuracy and performance metrics
  • • Understanding confusion matrix concepts
  • • Learning precision and recall metrics
  • • Understanding overfitting and underfitting
  • • Exploring cross validation techniques
  • • Building predictive analytics models

  • • 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

  • • 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

  • • Feature engineering exercise
  • • Dataset transformation task
  • • Data analysis report creation
  • • Correlation analysis task

  • • Movie recommendation system
  • • Customer personality analysis

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 And Mathematics Basics: Understand mean, median, mode, variance, standard deviation, probability, correlation, covariance, and data distribution concepts.
Data Collection And Cleaning: Work with csv files, missing values, duplicate records, incorrect data, outliers, and dataset 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, feature selection basics, 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, documenting work, 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, building basic models, and preparing project reports.

Data Analyst Intern:

Work on python-based data analysis, visualization, reporting, business insight generation, and dashboard support 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, charts, 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, visualization, and project work.
Industry-relevant Tools: The course covers python, numpy, pandas, matplotlib, seaborn, scikit-learn basics, jupyter notebook, and google colab.
Mentor-guided Project Support: Learners receive mentor support for concept clarity, data cleaning, visualization, coding practice, model building, and project development.
Strong Foundation For Advanced Data Science Learning: The course prepares learners for 3 months, 4 months, and 6 months advanced data science programs.

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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