*Update 2024: ChatGPT for Data Science added*
The Problem
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.
However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.
And how can you do that?
Read more
*Update 2024: ChatGPT for Data Science added*
The Problem
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.
However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.
And how can you do that?
*Update 2024: ChatGPT for Data Science added*
The Problem
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.
However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.
And how can you do that?
Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming)
Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture
The Solution
Data science is a multidisciplinary field. It encompasses a wide range of topics.
Understanding of the data science field and the type of analysis carried out
Mathematics
Statistics
Python
Applying advanced statistical techniques in Python
Data Visualization
Machine Learning
Deep Learning
Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.
So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2024.
We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.
Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save).
The Skills
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
Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail.
Why learn it?
Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section.
7. Machine Learning
The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow.
Why learn it?
Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines.
What you get
A $1250 data science training program
Active Q&A support
All the knowledge to get hired as a data scientist
A community of data science learners
A certificate of completion
Access to future updates
Solve real-life business cases that will get you the job
You will become a data scientist from scratch We are happy to offer an unconditional 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it.
Why wait? Every day is a missed opportunity.
Click the “Buy Now” button and become a part of our data scientist program today.
Here's a deal for you
UDEAFNULP2024
What's inside
Learning objectives
- The course provides the entire toolbox you need to become a data scientist
- Fill up your resume with in demand data science skills: statistical analysis, python programming with numpy, pandas, matplotlib, and seaborn, advanced statistical analysis, tableau, machine learning with stats models and scikit-learn, deep learning with tensorflow
- Impress interviewers by showing an understanding of the data science field
- Learn how to pre-process data
- Understand the mathematics behind machine learning (an absolute must which other courses don’t teach!)
- Start coding in python and learn how to use it for statistical analysis
- Perform linear and logistic regressions in python
- Carry out cluster and factor analysis
- Be able to create machine learning algorithms in python, using numpy, statsmodels and scikit-learn
- Apply your skills to real-life business cases
- Use state-of-the-art deep learning frameworks such as google’s tensorflowdevelop a business intuition while coding and solving tasks with big data
- Unfold the power of deep neural networks
- Improve machine learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
- Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
- Show more
- Show less
- The course provides the entire toolbox you need to become a data scientist
- Fill up your resume with in demand data science skills: statistical analysis, python programming with numpy, pandas, matplotlib, and seaborn, advanced statistical analysis, tableau, machine learning with stats models and scikit-learn, deep learning with tensorflow
- Impress interviewers by showing an understanding of the data science field
- Learn how to pre-process data
- Understand the mathematics behind machine learning (an absolute must which other courses don’t teach!)
- Start coding in python and learn how to use it for statistical analysis
- Perform linear and logistic regressions in python
- Carry out cluster and factor analysis
- Be able to create machine learning algorithms in python, using numpy, statsmodels and scikit-learn
- Apply your skills to real-life business cases
- Use state-of-the-art deep learning frameworks such as google’s tensorflowdevelop a business intuition while coding and solving tasks with big data
- Unfold the power of deep neural networks
- Improve machine learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
- Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
- Show more
- Show less
Syllabus
Part 1: Introduction
A Practical Example: What You Will Learn in This Course
What Does the Course Cover
Download All Resources and Important FAQ
Read more
Syllabus
Good to know
Save this course
Save
Reviews summary
In-depth data science immersion
The course is led by knowledgeable and passionate instructors.
The course provides hands-on experience with real-world data science tools and techniques.
The course covers a wide range of topics in great depth.
Activities
Review Probability and Statistics
Show steps
Strengthens the understanding of probability and statistics concepts, providing a solid foundation for the statistical analysis and modeling techniques covered in this course.
Browse courses on
Probability
Show steps
-
Review the concepts of probability, random variables, and probability distributions.
-
Practice solving probability problems involving conditional probability and Bayes' theorem.
-
Solve practice problems involving statistical measures such as mean, median, and standard deviation.
-
Review hypothesis testing and confidence intervals.
Review Python basics
Show steps
Refreshes prior Python skills, ensuring a strong foundational understanding of the language for this course's topics.
Browse courses on
Python
Show steps
-
Review Python data types, variables, and operators.
-
Practice writing simple Python functions.
-
Complete a Python coding challenge or practice exercise.
Follow tutorials on advanced statistical techniques
Show steps
Expands knowledge and expertise in advanced statistical techniques, enhancing analytical capabilities and improving understanding.
Show steps
-
Locate online tutorials or courses on advanced statistical methods such as regression analysis, time series analysis, or machine learning.
-
Follow the tutorials and complete the accompanying exercises.
-
Apply the learned techniques to analyze real-world datasets.
One other activity
Expand to see all activities and additional details
Show all four activities
Develop a predictive model using machine learning
Show steps
Provides a comprehensive understanding of the machine learning process, fostering hands-on experience and improving problem-solving skills.
Show steps
-
Identify a business problem that can be solved using a predictive model.
-
Collect and prepare the relevant data.
-
Select and train a machine learning algorithm.
-
Evaluate the performance of the model.
-
Deploy the model into a production environment.
Review Probability and Statistics
Show steps
Strengthens the understanding of probability and statistics concepts, providing a solid foundation for the statistical analysis and modeling techniques covered in this course.
Browse courses on
Probability
Show steps
- Review the concepts of probability, random variables, and probability distributions.
- Practice solving probability problems involving conditional probability and Bayes' theorem.
- Solve practice problems involving statistical measures such as mean, median, and standard deviation.
- Review hypothesis testing and confidence intervals.
Review Python basics
Show steps
Refreshes prior Python skills, ensuring a strong foundational understanding of the language for this course's topics.
Browse courses on
Python
Show steps
- Review Python data types, variables, and operators.
- Practice writing simple Python functions.
- Complete a Python coding challenge or practice exercise.
Follow tutorials on advanced statistical techniques
Show steps
Expands knowledge and expertise in advanced statistical techniques, enhancing analytical capabilities and improving understanding.
Show steps
- Locate online tutorials or courses on advanced statistical methods such as regression analysis, time series analysis, or machine learning.
- Follow the tutorials and complete the accompanying exercises.
- Apply the learned techniques to analyze real-world datasets.
Develop a predictive model using machine learning
Show steps
Provides a comprehensive understanding of the machine learning process, fostering hands-on experience and improving problem-solving skills.
Show steps
- Identify a business problem that can be solved using a predictive model.
- Collect and prepare the relevant data.
- Select and train a machine learning algorithm.
- Evaluate the performance of the model.
- Deploy the model into a production environment.
Career center
Data Scientist
Machine Learning Engineer
Statistician
Data Analyst
Operations Research Analyst
Data Architect
Risk Analyst
Data Engineer
Quantitative Analyst
Actuary
Market Researcher
Business Analyst
Database Administrator
Financial Analyst
Software Engineer
Reading list
Save
Save
Share
Similar courses
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
