Introduction to Data Science
This course is part of multiple programs.
- Taught in English
- 19 Languages Available
- Some content may not be translated
Instructor:
Ravi Kishore
Financial aid available
282,513 already enrolled
Course
Gain insight into a topic and learn the fundamentals
- 4.7
- (11,910 reviews)
- 46%
Medium Level
Recommended experience
5 hours (approximately)
Flexible schedule
Learn at your own pace
What you'll learn
- The course provides the entire toolbox you need to become a data scientist
- Impress interviewers by showing an understanding of the data science field
- Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
- Perform linear and logistic regressions in Python
- Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
- Learn how to pre-process data
- Start coding in Python and learn how to use it for statistical analysis
- Carry out cluster and factor analysis
- Apply your skills to real-life business cases
- Unfold the power of deep neural networks
Skills you'll gain
  1. Intro to Data and Data Science
Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean?
Why learn it? As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem. This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science.
2. Mathematics
Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail.
We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on.
Why learn it?
Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal.
3. Statistics
You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist.
Why learn it?
This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist.
4. Python
Python is a relatively new programming language and, unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. Where Python really shines however, is when it deals with machine and deep learning.
Why learn it?
When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language.
5. Tableau
Data scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science.
Why learn it?
A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers.
6. Advanced Statistics
Details to know
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Assessments
4 quizzes
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Build your subject-matter expertise
Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 3 modules in this course
- You should take this course if you want to become a Data Scientist or if you want to learn about the field
- This course is for you if you want a great career
- The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills
- Data Science and Business Buzzwords: Why are there so Many?
- What is the difference between Analysis and Analytics
- What is the difference between Analysis and Analytics
- Data Science and Business Buzzwords: Why are there so Many?
- What is the difference between Analysis and Analytics
- What is the difference between Analysis and Analytics
- Techniques for Working with Traditional Data
- Techniques for Working with Traditional Data
- Real Life Examples of Traditional Data
- Techniques for Working with Big Data
- Techniques for Working with Traditional Data
- Techniques for Working with Traditional Data
- Real Life Examples of Traditional Data
- Techniques for Working with Big Data
- The Basic Probability Formula
- Computing Expected Values
- Frequency
- The Basic Probability Formula
- Computing Expected Values
- Frequency
- How should we define AI?
- Related fields
- Philosophy of AI
- Search and problem solving
- Solving problems with AI
- Search and games
- Odds and probability
- The Bayes rule
- Naive Bayes classification
- The types of machine learning
- The nearest neighbor classifier
- Regression
- Neural network basics
- How neural networks are built
- Advanced neural network techniques
- About predicting the future
- The societal implications of AI
- Summary
Instructor Profile
- Instructor ratings
- 4.1 (3,466 ratings)
Instructor:
Ravi Kishore
12 Courses, 2,095,931 learners
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Recommended if you're interested in Data Science
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Learner reviews
- 4.7 11,910 reviews
Reviewed on Sep 12, 2023
I found this course very approachable and informative. My background is in psychology and I was able to follow along and complete the course. I now feel much more aware of the current state of AI.