DATA-SCI.AW1
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.
- 11 Interactive Lessons and 85 topics mapped to the official exam objectives
Intermediate Self-paced · 1 year access
11Interactive Lessons
85Topics
01 / Skills you'll get
What you will be able to do
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.
- 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.
Course Highlights
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11 Structured Lessons Comprehensive coverage of core course objectives
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1 Year Full Access Self-paced learning accessible anytime on all devices
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.
Keep going
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Start your journey toward a great career with Data Science.
- 1 year of full access
- Certificate of completion