Course description

Data science has emerged as a critical discipline driving innovation, efficiency, and decisionmaking across various industries. Here are key reasons why data science is essential in the real world:

  • Informed Decision Making
  • Business Efficiency and Optimization
  • Personalized Customer Experiences
  • Healthcare and Medical Advancements
  • Urban Planning and Smart Cities
  • Scientific Research and Innovation
  • Financial and Risk Management
  • Education and Learning

  • Data science has significantly influenced and expanded the scope of computer science, leveraging advanced algorithms, computational power, and statistical techniques to extract insights and knowledge from data.
  • Here's an overview of how data science intersects with various domains within computer science:

  • Data science is at the forefront of driving innovation and transformation within computer science, enhancing capabilities in machine learning, big data analytics, and artificial intelligence.
  • As data becomes increasingly abundant and complex, the integration of data science principles and techniques is crucial for solving challenging problems and advancing technology across various domains.
  • At Stellaraa, we offer specialized courses to equip individuals with the skills and knowledge needed to excel in data science and its applications within computer science.

  • Overview of Data Science
  • Importance and Applications of Data Science
  • Data Science Workflow
  • Tools and Technologies in Data Science
  • Setting Up the Development Environment (Python, Jupyter Notebooks, Anaconda)

  • Types of Data: Structured, Unstructured, Semi-Structured
  • Data Collection Methods
  • Data Cleaning and Handling Missing Values
  • Exploratory Data Analysis (EDA)
  • Data Visualization Techniques
  • Feature Engineering and Selection

  • Python for Data Science
    1. Introduction to Python
    2. Data Structures and Functions
    3. Libraries: NumPy, Pandas, Matplotlib, Seaborn
  • R for Data Science (Optional)
    1. Introduction to R
    2. Data Manipulation with dplyr
    3. Data Visualization with ggplot2

  • Descriptive Statistics
  • Inferential Statistics
  • Probability Theory
  • Hypothesis Testing
  • Statistical Distributions
  • Sampling Techniques

  • Importance of Data Visualization
  • Data Visualization Tools and Libraries
    1. Matplotlib and Seaborn in Python
    2. Tableau and Power BI
  • Creating Effective Data Visualizations
  • Dashboard Design and Storytelling with Data

  • Supervised Learning
    1. Regression Algorithms (Linear Regression, Ridge, Lasso)
    2. Classification Algorithms (Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, SVM)
    3. Evaluation Metrics (MSE, RMSE, R², Accuracy, Precision, Recall, F1 Score, ROC-AUC)
  • Unsupervised Learning
    1. Clustering Algorithms (K-Means, Hierarchical Clustering, DBSCAN)
    2. Dimensionality Reduction (PCA, LDA, t-SNE)
  • Model Selection and Evaluation
    1. Cross-Validation
    2. Hyperparameter Tuning (Grid Search, Random Search)

  • Ensemble Methods
    1. Bagging, Boosting (AdaBoost, Gradient Boosting, XGBoost)
    2. Stacking and Blending
  • Deep Learning Basics
    1. Introduction to Neural Networks
    2. Using TensorFlow and Keras
  • Natural Language Processing (NLP)
    1. Text Preprocessing
    2. Text Classification and Sentiment Analysis
    3. Word Embeddings (Word2Vec, GloVe)
  • Time Series Analysis
    1. Introduction to Time Series Data
    2. ARIMA and SARIMA Models
    3. Forecasting Techniques

  • Introduction to Big Data
  • Hadoop Ecosystem
  • Working with Apache Spark
  • Big Data Storage and Processing
  • Use Cases of Big Data in Data Science

  • Introduction to Data Engineering
  • ETL Processes
  • Data Warehousing Concepts
  • Working with SQL and NoSQL Databases
  • Data Pipeline Tools (Apache Airflow, Luigi)

  • Introduction to Cloud Computing
  • Using AWS for Data Science (S3, Redshift, SageMaker)
  • Using Google Cloud Platform (BigQuery, Cloud ML Engine)
  • Using Microsoft Azure (Azure ML, Cosmos DB)

Tool 1

Python with Data Science Libraries

Tool 2

R

Tool 3

Jupyter Notebooks

Tool 4

Tableau

Tool 5

Apache Spark

Skill level

Expiry period

Lifetime
Annual Salary
Source: Glassdoor
Hiring Companies
American Express Honeywell Accenture Wipro
Source: Indeed
Annual Salary
Source: Glassdoor
Hiring Companies
American Express Honeywell Accenture Wipro
Source: Indeed
Annual Salary
Source: Glassdoor
Hiring Companies
American Express Honeywell Accenture Wipro
Source: Indeed
Annual Salary
Source: Glassdoor
Hiring Companies
American Express Honeywell Accenture Wipro
Source: Indeed

Why Stellaraa

Comprehensive Course
Expert Instruction
Live Interaction
Internship
Capstone Project
LMS Access
Corporate Certificate
One on One Interaction
Career Support

Projects

Sentiment Analysis of Product Reviews

Fraud Detection in Financial Transactions

Speech Recognition System

Predicting Diabetes Using Machine Learning

Human Activity Recognition Using Smartphones

Customer Segmentation Using K-Means Clustering

Spam Email Detection

Customer Churn Prediction

CERTIFICATIONS

Certificate 1
Certificate 2
Certificate 3
5999

Essential

  • 6+ Hrs of Live Sessions
  • Industrial Projects
  • Recorded videos
  • Certifications
  • Mentor Support
  • One On One Doubt Clearing Sessions
  • Placement Guidance
  • Interview Assistance
Select Plan
14999

Elite

  • 24+ Hrs of Live Sessions
  • Industrial Projects
  • Recorded videos
  • Certifications
  • Mentor Support
  • One On One Doubt Clearing Sessions
  • Placement Guidance
  • Interview Assistance
Select Plan

Discover Top Categories

Data Analysis
Machine Learning & Deep Learning
Statistics & Probability
Data Wrangling & Cleaning
Data Visualization
Programming Languages
Big Data & Distributed Computing
Cloud Computing
Natural Language Processing
Domain Expertise

Frequently asked question

  • The Stellaraa Data Science Course provides a comprehensive introduction to the field of data science. You'll learn about data collection, analysis, and visualization techniques, along with the tools and technologies used by data scientists.

  • This course is ideal for anyone interested in learning the fundamentals of data science. Whether you're a complete beginner or have some prior experience, this course will equip you with the skills you need to get started in this exciting field.

  • There are no specific prerequisites for this course. However, a basic understanding of mathematics and statistics will be helpful.

  • The Stellaraa Data Science Course covers a wide range of topics, including:

  • Introduction to data science
  • Data collection and wrangling
  • Data analysis techniques
  • Data visualization
  • Machine learning basics
  • Big data concepts

  • The Stellaraa Data Science Course is delivered through a combination of online lectures, video tutorials, hands-on exercises, and quizzes.

  • By taking the Stellaraa Data Science Course, you will gain the skills and knowledge you need to:

  • Understand the data science lifecycle
  • Collect, clean, and analyze data
  • Create informative data visualizations
  • Apply machine learning techniques to solve real-world problems
  • Pursue a career in data science

Recognized By

Credential Platform Partners

WIPRO

Contact us

Connect with us to experience seamless communication. we value open dialogue and are eager to engage with you. whether you have questions, ideas, or feedback, we are here to listen and respond.

Email

support@stellaraa.com,
collabs@stellaraa.com

Get in touch

+91 6379 937 947

Our address

59/1,4thmain, Dattathreya Nagar, Hoskerehalli, Banashankari Iii Stage, Bangalore, Bangalore South, Karnataka, India, 560085

Office hours

11:00 AM - 8:00 PM

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