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.

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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