Data Science

Data Science Crash Course

You will learn the latest technologies and best practices in the industry.

  • 8 Weeks
  • Fresher

Course Description

With the growing demand for data-driven decision-making across industries, professionals skilled in data science can work in various sectors like finance, healthcare, e-commerce, marketing, and more. Continuous learning and staying updated with emerging technologies are vital to thrive in this dynamic field.

What will you learn?

By the end of the course,

  • Understand the Fundamentals
  • Data Analysis
  • Learn SQL
  • Machine Learning Models
  • Tableau
  • Power BI

Requirements

This tutorial will help you learn quickly and thoroughly.

  • Computer Access: Participants must have access to a computer with sufficient processing power and memory to run game development software smoothly.
  • Operating System: Windows, macOS, or Linux operating systems are recommended, depending on the game engine being used.
  • Internet Connection: A stable internet connection is necessary for accessing online resources, tutorials, and potentially participating in virtual classes.

Syllabus

  • Module 1: Introduction to Data Science and Python

    1. Introduction to Data Science

    • Understanding the Data Science process
    • Role and responsibilities of a Data Scientist

    2. Introduction to Python

    • Basics of Python programming
    • Data types, operators, and expressions
    • Control flow and loops

    3. Data Handling with Python

    • Working with data structures: lists, tuples, dictionaries, etc.
    • Reading and writing data with Python
    • Data cleaning and preprocessing
  • Module 2: Statistics and Data Visualization

    1. Fundamentals of Statistics

    • Descriptive statistics
    • Probability theory and distributions
    • Inferential statistics

    2. Statistical Analysis with Python

    • Using libraries like NumPy, SciPy, and Pandas for statistical analysis

    3. Data Visualization with Python

    • Introduction to Matplotlib and Seaborn
    • Creating various types of plots and charts
  • Module 3: SQL and Data Manipulation

    1. Introduction to SQL

    • Basics of relational databases
    • SQL syntax and queries

    2. Data Manipulation with SQL

    • Querying and joining multiple tables
    • Data aggregation and transformation
  • Module 4: Machine Learning with Python

    1. Introduction to Machine Learning

    • Understanding supervised, unsupervised, and reinforcement learning
    • Model evaluation and selection

    2. Supervised Learning Algorithms

    • Regression (linear, logistic)
    • Classification (decision trees, SVM, KNN)
    • Model evaluation and hyperparameter tuning

    3. Unsupervised Learning Algorithms

    • Clustering (K-means, hierarchical)
    • Dimensionality reduction (PCA)
  • Module 5: Deep Learning

    1. Introduction to Deep Learning

    • Neural network basics
    • Activation functions, loss functions, and optimization algorithms

    2. Deep Learning Frameworks (e.g., TensorFlow, Keras)

    • Building and training neural networks
    • Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
  • Module 6: Data Visualization with Tableau and Power BI

    1. Introduction to Tableau

    • Basics of Tableau
    • Creating visualizations and dashboards

    2. Introduction to Power BI

    • Basics of Power BI
    • Creating visualizations and reports
  • Module 7: Capstone Project (Duration: 1 month)
    • Applying the acquired knowledge and skills to work on a real-world data science project, utilizing Python, SQL, and visualization tools.

Software tools we covered

Students typically use a variety of software tools and development environments to learn and practice their skills.

  • SAS
  • Apache Spark
  • BigML
  • D3
  • MATLAB
  • Tableau

Frequently asked question?

Data science is an interdisciplinary field that uses various techniques, algorithms, processes, and systems to extract knowledge and insights from structured and unstructured data. It combines elements of statistics, computer science, machine learning, and domain expertise to solve complex problems and make data-driven decisions.

Key skills for a data scientist include programming (e.g., Python or R), statistical analysis, data visualization, machine learning, data cleaning and preprocessing, domain knowledge, and strong communication skills.

The data science process typically involves stages such as data collection, data cleaning and preprocessing, exploratory data analysis, feature engineering, model building and training, model evaluation, and deployment.

Data science is a broader field that encompasses various techniques for data analysis and interpretation. Machine learning is a subset of data science that focuses on training algorithms to make predictions or decisions based on data. Artificial intelligence (AI) goes beyond data analysis and includes the development of systems that can perform tasks that typically require human intelligence.

Data is the foundation of data science. It is the raw material from which insights are extracted. High-quality, relevant, and well-structured data is essential for accurate analysis and modeling.

Cherish the Experiences

Our courses will help you become a confident and competent software developer. You will learn the latest technologies and best practices in the industry.

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