Data Science Foundations

  • 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 simple predictive solutions.
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Data Science Foundations
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Course Overview

Data Science is the process of collecting, cleaning, analyzing, visualizing, and interpreting data to solve real-world problems and make predictions. This 1 month course helps learners build a strong foundation in Python basics, statistics, data preprocessing, data analysis, visualization, machine learning basics, and beginner-level 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.

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

Course Content

  • • introduction to data science and analytics
  • • applications of data science in real-world industries
  • • understanding the data science workflow
  • • setting up python environment for data science
  • • introduction to jupyter notebook & google colab
  • • installing essential libraries
  • • understanding data types and structures in python

  • • variables and data types
  • • conditional statements
  • • loops and iterations
  • • functions and reusable code
  • • working with lists, tuples, sets, and dictionaries
  • • string manipulation for data cleaning
  • • file handling (csv, txt files)
  • • exception handling basics

  • • introduction to numpy arrays
  • • difference between lists and arrays
  • • array creation and manipulation
  • • indexing, slicing, and reshaping arrays
  • • mathematical and statistical operations
  • • aggregation functions
  • • random number generation
  • • broadcasting in numpy

  • • introduction to pandas
  • • series and dataframes
  • • reading csv, excel, and json files
  • • data selection and filtering
  • • data cleaning techniques
  • • handling missing values
  • • sorting and grouping data
  • • merging and joining datasets
  • • aggregation and summary statistics

  • • importance of data visualization
  • • introduction to matplotlib
  • • line charts
  • • bar charts
  • • pie charts
  • • histograms
  • • scatter plots
  • • introduction to seaborn
  • • heatmaps and statistical visualizations

  • • mean, median, mode
  • • standard deviation & variance
  • • probability basics
  • • data distribution
  • • correlation and relationship between variables
  • • outlier detection

  • • handling missing data
  • • removing duplicate data
  • • data formatting and transformation
  • • encoding categorical data
  • • feature scaling basics
  • • outlier treatment techniques

  • • what is machine learning?
  • • types of machine learning
  • • supervised vs unsupervised learning
  • • model training concepts
  • • introduction to scikit-learn
  • • regression model
  • • classification concepts
  • • how to check model performance

  • • use dataset of iris and handle missing values
  • • train ml regression model on house price prediction

  • • build ml model on cancer detection

Skills Developed with Data Science Course

Python For Data Science: Learn python fundamentals, variables, data types, conditions, loops, functions, 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, and basic data preparation techniques.
Numpy And Pandas: Practice arrays, dataframes, filtering, sorting, grouping, merging, transformation, and basic 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 how datasets are prepared for models.
Data Science Tools Usage: Work with python, jupyter notebook or 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 excel/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 45 days, 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.
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