DATA-SCI.AW1 ISBN: 978-1-64459-628-9
Data Science Fundamentals and Practical Approaches
Learn everything you need about Data Science in one course and get skilled with Big Data Analysis and Python programming.
What you will be able to do
- Understand the role of SQL in data science
- Learn to handle Data science with tools like TensorFlow, and PyTorch.
- Deploy CNN models.
- Explore the Data analytics lifecycle.
- Implement various data preprocessing operations.
- Analyze possible data error types.
- Learn visual encoding with data visualization software.
- Explore the data visualization libraries.
- Utilize the role of Statistics & Machine Learning (ML) in data science.
- Learn about the seven layers of social media & business analytics.
- Interact with Big Data & HDFS from Python applications.
Intermediate Self-paced · 1 year access
01 / About
About This Course
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No credit card required
Learn the fundamentals of the Data Science course with our comprehensive training plan and master data preprocessing, visualization & analysis like a pro!
Implement Data analysis techniques with practical lessons & hands-on labs to solve any business problems with statistics & media analytics.
Learn with instances from real-world experiences and handle data with perfect tools.
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
11 Interactive Lessons · 85 topics01 Preface +
02 Fundamentals of Data Science 12 topics +
- Introduction to data science
- Why learn data science?
- Data analytics lifecycle
- Types of data analysis
- Types of jobs in data analytics
- Data science tools
- Fundamental areas of study in data science
- Role of SQL in data science
- Pros and cons of data science
- Conclusion
- References
- Points to remember
03 Data Preprocessing 7 topics +
- Introduction to data preprocessing
- Data types and forms
- Possible data error types
- Various data preprocessing operations
- Conclusion
- References
- Points to remember
04 Data Plotting and Visualization 12 topics +
- Introduction to data visualization
- Visual encoding
- Data visualization software
- Data visualization libraries
- Basic data visualization tools
- Specialized data visualization tools
- Advanced data visualization tools
- Visualization of geospatial data
- Data visualization types
- Conclusion
- References
- Points to remember
05 Statistical Data Analysis 6 topics +
- Role of statistics in data science
- Kinds of statistics
- Probability theory
- Conclusion
- References
- Points to remember
06 Machine Learning for Data Science 7 topics +
- Overview of machine learning
- Supervised machine learning
- Unsupervised machine learning
- Reinforcement learning
- Conclusion
- References
- Points to remember
07 Time-Series Analysis 6 topics +
- Overview of time-series analysis
- Components of time-series
- Time-series forecasting models
- Conclusion
- References
- Points to remember
08 Deep Learning for Data Science 10 topics +
- Introduction to TensorFlow
- Pytorch
- Deep learning primitives
- Convolutional Neural Network (CNN)
- TensorFlow and CNN
- CNN and data analysis
- AutoEncoder
- Conclusion
- References
- Points to remember
09 Social Media Analytics 9 topics +
- Overview of social media analytics
- Seven layers of social media analytics
- Social media analytics cycle
- Key social media analytics methods
- Accessing social media data
- Challenges to social media analytics
- Conclusion
- References
- Points to remember
10 Business Analytics 8 topics +
- An overview of business analytics
- The business analytics lifecycle
- Basic tools used in business analytics
- Main applications in business analytics
- Challenges faced in business analytics
- Conclusion
- References
- Points to Remember
11 Big Data Analytics 8 topics +
- An overview of Big Data
- Hadoop
- HDFS (Hadoop Distributed File System)
- Interacting with HDFS
- Interacting with HDFS from Python applications
- Conclusion
- References
- Points to remember
03 / FAQs
Questions before you start
Is this a basic Data Science course? +
This course is an introduction to data science. It teaches you the concepts from scratch to help you build strong software in the future & data principles from the foundation.
Who should take this course?+
Individuals from various fields & students interested in data science & programming can start this course & learn from the basics. Individuals interested in AI & ML can also learn this course.
What tools and technologies will be learned?+
Learn data analysis, CNN tools such as TensorFlow & PyTorch, Machine learning & Big data with our interactive course. Work through hands-on labs & create practical changes with practice.
Will I learn AI and machine learning in this course? +
Absolutely! We’ll teach you the basics of AI & building networks, and get you started with machine learning using Python and TensorFlow.
Is this course suitable for career changers?+
Yes, absolutely! The course includes knowledge from various domains and can be a game changer for career changers with its unique approach and great offerings in data science.
Can this course help me get a job?+
This course can help you get an entry-level job in data analysis, however, it is prescribed to go for a more detailed certification like CompTIA, ISC2, & Axelos.
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