Data Science

Last Updated : April 23, 2024

Bim Academy

31 Lessons

0 Enrolled

About Course

Organizations rely on data scientists to extract valuable insights, make data-informed decisions, and drive innovation in today’s data-driven world. Our program offers a comprehensive curriculum that covers the essential concepts, techniques, and tools used in data science. From data manipulation and analysis to machine learning algorithms and big data processing, you’ll gain a solid foundation in the key aspects of data science. Unleash the power of data, become a proficient data scientist, and make a meaningful impact in your career.

Foundations: Learn the basics of data science, Python programming, and the data science lifecycle.
Data Analysis: Master techniques for data manipulation, exploratory data analysis, and visualization.
Statistical Skills: Develop skills in descriptive and inferential statistics, probability, and hypothesis testing.
Machine Learning: Explore supervised and unsupervised learning algorithms, model evaluation, and big data processing.
Natural Language Processing: Gain insights into text processing, sentiment analysis, and language generation.
Advanced Topics & Projects: Delve into advanced topics like ensemble learning and deep learning, applying your skills to real-world data science projects.

Course Content

Python Programming

Introduction to Data Science

Data Manipulation and Analysis

Statistics and Probability

Machine Learning Algorithms

Big Data and Distributed Computing

Natural Language Processing (NLP)

Time Series Analysis

Data Visualization and Communication

Production Deployment

Advanced Topics in Data Science

Capstone Project

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Free

Data Science

About Course

What Will You Learn?

Course Content

Python Programming

Introduction to Data Science

Understanding the role of data science in Industry and society

Overview of the data science lifecycle and process

Introduction to key concepts and tools used in data science

Data Manipulation and Analysis

Data acquisition and cleaning technique

Exploratory data analysis and visualization

Data transformation and feature engineering

Statistics and Probability

Descriptive and inferential statistics

Probability distributions and hypothesis testing

Statistical modelling and regression analysis

Machine Learning Algorithms

Supervised learning algorithms

Unsupervised learning algorithms

Model evaluation, selection, and validation techniques

Big Data and Distributed Computing

Introduction to big data and its challenges

Distributed computing frameworks (e.g., Hadoop, Spark)

Natural Language Processing (NLP)

Basics of NLP and text processing

Text classification and sentiment analysis

Named entity recognition and language generation

Time Series Analysis

Introduction to time series data

Time series forecasting techniques

Seasonality and trend analysis

Data Visualization and Communication

Principles of effective data visualization

Tools and techniques for data visualization

Production Deployment

Strategies for deploying ML models in production

Scalability, performance, and monitoring considerations

Integration with existing systems and infrastructure

Advanced Topics in Data Science

Ensemble learning techniques (e.g., bagging, boosting)

Anomaly detection and outlier analysis

Deep learning and neural networks

Capstone Project

Apply the knowledge and skills acquired throughout the program

Work on a real-world data science project from data exploration to model development and evaluation

Showcase proficiency in data analysis, machine learning, and communication skills

Student Ratings & Reviews

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