Applied Data Science

  • High Demand In The It Industry: Data science is used by companies to analyze large datasets, predict outcomes, understand customers, detect risks, and support data-driven decisions.
  • Useful Across Multiple Industries: Data science is used in healthcare, finance, education, e-commerce, marketing, retail, business intelligence, and technology companies.
  • Build Strong Analytical And Prediction Skills: Learners understand how to convert raw data into insights, dashboards, reports, predictive models, and business solutions.
4 Months ₹37,999 ₹29,999
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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 accurate predictions. This 4 months course helps learners build advanced Data Science skills using Python, statistics, data preprocessing, exploratory data analysis, machine learning, feature engineering, model evaluation, advanced ML techniques, deep learning basics, and industry-level Data Science projects.

Course with Live Project

No Refund Available

Advanced Data Science Workflow: Learners Understand The Complete Workflow From Data Collection And Cleaning To Analysis, Visualization, Machine Learning, Evaluation, Optimization, And Project Presentation.

Machine Learning And Predictive Modeling: Work With Regression, Classification, Clustering, Feature Engineering, Model Evaluation, Ensemble Learning, And Prediction-based Solutions Using Python.

Industry-level Data Science Projects: Develop Practical Projects Like Disease Prediction Platforms, Smart Career Guidance Engines, Resume Ranking Systems, Sales Forecasting, And Recommendation Models.

Course Content

  • • Understanding data science fundamentals
  • • Exploring data science lifecycle stages
  • • Learning real-world data applications
  • • Understanding ai ml data differences
  • • Exploring data science career roles
  • • Understanding business problem solving
  • • Setting up data science environment
  • • Learning industry workflow concepts

  • • Learning python programming fundamentals
  • • Understanding variables and data types
  • • Working with conditional statements logic
  • • Using loops for data processing tasks
  • • Creating functions and reusable modules
  • • Managing data using python collections
  • • Handling files and data inputs
  • • Managing errors and exceptions
  • • Using python libraries for data science

  • • Understanding mean median mode concepts
  • • Learning variance and standard deviation
  • • Exploring probability and statistics basics
  • • Understanding correlation between variables
  • • Learning linear algebra fundamentals
  • • Working with vectors and matrices
  • • Understanding data distribution patterns
  • • Applying statistical thinking in data science
  • • Learning optimization techniques basics

  • • Understanding numpy array operations
  • • Performing mathematical data operations
  • • Using pandas for data analysis
  • • Working with dataframes efficiently
  • • Reading csv and excel files
  • • Cleaning and transforming datasets
  • • Handling missing values properly
  • • Performing exploratory data analysis
  • • Extracting insights from data

  • • Understanding data visualization concepts
  • • Creating charts and graphs
  • • Using matplotlib visualization library
  • • Using seaborn statistical visualizations
  • • Building correlation heatmaps
  • • Creating interactive visual dashboards
  • • Presenting insights through storytelling
  • • Designing professional reports

  • • Understanding machine learning concepts
  • • Learning types of machine learning
  • • Working with supervised learning models
  • • Understanding regression algorithms
  • • Exploring classification algorithms
  • • Learning unsupervised learning techniques
  • • Working with clustering algorithms
  • • Measuring model accuracy performance
  • • Training models using real datasets
  • • Evaluating machine learning models

  • • Understanding feature selection methods
  • • Creating new informative features
  • • Handling missing data properly
  • • Encoding categorical variables
  • • Scaling and normalization techniques
  • • Removing outliers from data
  • • Improving dataset quality efficiently
  • • Preparing data for modeling

  • • Understanding ensemble learning methods
  • • Bagging and boosting techniques
  • • Random forest and xgboost basics
  • • Handling imbalanced datasets
  • • Model explainability concepts
  • • Feature importance analysis
  • • Hyperparameter tuning methods
  • • Improving model performance

  • • Understanding neural network concepts
  • • Artificial neural network structure
  • • Activation functions explained
  • • Forward and backpropagation concepts
  • • Introduction to tensorflow and keras
  • • Building simple neural networks
  • • Deep learning applications overview

  • • Understanding recommendation systems
  • • Introduction to natural language processing
  • • Introduction to computer vision concepts
  • • Fraud detection system basics
  • • Healthcare prediction systems overview
  • • Business intelligence applications
  • • Ethical ai and responsible usage

  • • Advanced feature engineering
  • • Deep learning introduction task
  • • Model optimization exercise
  • • Recommendation logic design
  • • Data science case study
  • • Real dataset processing

  • • Disease prediction platform
  • • Smart career guidance engine
  • • Resume ranking intelligence system

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-based tasks.
Statistics And Mathematics: Understand probability, correlation, variance, standard deviation, linear algebra basics, distributions, hypothesis concepts, and statistical thinking.
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 and excel 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, transformation, encoding, scaling, and improving dataset quality for better model performance.
Advanced Ml And Deep Learning Basics: Work with ensemble learning, random forest, boosting, xgboost basics, model tuning, neural networks, and tensorflow/keras basics.
Data Science Project Development: Practice planning, cleaning data, analyzing datasets, building models, evaluating performance, documenting workflow, and presenting industry-level projects.

Career Opportunities after Data Science Course

This course opens doors to multiple high-demand career paths across industries.

Data Scientist:

Analyze data, build predictive models, generate insights, and support decision-making using statistics, python, and machine learning.

Machine Learning Engineer:

Build, train, evaluate, optimize, and deploy ml models for real-world prediction and automation systems.

Data Analyst:

Clean, analyze, visualize, and report data to support business decisions and performance improvement.

Predictive Modeling Analyst:

Build models for forecasting, risk analysis, customer behavior, business predictions, and performance improvement.

Data Science Project Associate:

Support dataset preparation, feature engineering, model testing, documentation, project execution, and insight presentation.

Why Enroll in Data Science with Solitaire Learning?

Advanced Practical Curriculum: The course starts from python and statistics, then moves into data analysis, machine learning, feature engineering, deep learning basics, and projects.
Real Dataset-based Learning: Learners work with practical datasets from healthcare, finance, e-commerce, business, marketing, and customer analytics domains.
Industry-relevant Tools: The course covers python, numpy, pandas, matplotlib, seaborn, scikit-learn, tensorflow/keras basics, xgboost basics, jupyter notebook, and google colab.
Mentor-guided Project Support: Learners receive mentor guidance for data cleaning, visualization, model building, debugging, optimization, portfolio projects, and interview preparation.
Strong Career And Portfolio Preparation: The course helps learners become career-ready by covering data science workflows, predictive modeling, ml projects, and industry-level project presentation.

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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