First We need to understand what is data Science ?
The use of the term Data Science is becoming more common, but how to learn data science ?. What does it mean exactly? What skills do I need to become a Data Scientist? What is the difference between BI and Data Science? How are decisions and forecasts made in data science? These are some of the questions that will be answered later. Let’s first look at what Data Science is. Data Science is a combination of several tools, algorithms, and machine learning principles to discover hidden models from raw data. How does this differ from what statisticians have been doing for years?The answer lies in the difference between explaining and predicting.
As you can see in the picture above, a data analyst usually explains what happens when processing the data history. On the other hand, Data Scientist not only performs exploratory analysis to discover their perceptions, but also uses several advanced machine learning algorithms to identify the occurrence of a particular event in the future. A data expert will look at the data from several angles, sometimes from previously unknown angles.
As a result, Data Science is primarily used to make decisions and predictions using predictive causal analysis, normative analysis (predictive and decisional science), and machine learning.
Predictive causal analysis:
If you want a model that can predict the possibilities of a particular event in the future, you need to apply predictive causal analysis. For example, if you provide money on credit, the likelihood of customers making their credit payments on time is a concern for you. Here, you can create a model that can perform a predictive analysis of the customer’s payment history to predict whether future payments will be made on time or not.
If you want a model with the intelligence to make your own decisions and the ability to modify it with dynamic parameters, you certainly need a prescriptive analysis. This relatively new area is providing advice. In other words, it not only predicts, but suggests a range of prescribed actions and associated results.
The best example of this is the Google car, which I also mentioned earlier. The data collected by the vehicles can be used to form autonomous cars.
You can run algorithms on these data to bring you intelligence. This will allow your car to make decisions such as when to turn, which route to take, when to slow down or speed up.
Automatic learning to make predictions:
If you have transactional data from a financial company and need to create a model to determine the future trend, machine learning algorithms are the best option. This is part of the paradigm of supervised learning. It is called supervised because you already have data on which you can train your machines. For example, a fraud detection model can be formed using a history of fraudulent purchases.
Auto discovery for model discovery:
If you do not have the parameters in which you can make predictions, you must discover the hidden models in the dataset to be able to make meaningful predictions. This is just the unsupervised model because it has no predefined tags to group together. The most commonly used algorithm for model discovery is clustering.
Let’s say you work in a telephone company and you have to establish a network by placing towers in a region. Then, you can use the grouping technique to find the locations of the towers, which will ensure that all users receive optimal signal strength.
Let’s see how the proportion of approaches described above differs for data analysis and data science. As you can see in the image below, the data analysis includes descriptive analysis and predictions to a certain extent. On the other hand, Data Science contains more information on predictive causal analysis and machine learning.
How to Learn Data Science?
During the last year, I taught myself data science. I learned hundreds of resources online and studied 6 to 8 hours every day. All while working for a minimum wage in a daycare. My goal was to start a career that I was passionate about, despite my lack of funds. Because of this choice, I have achieved a lot in the last months. I published my own website, I was published in a major online data science publication and I got scholarships for a competitive computer graduate program.
In the next article, I give you guidelines and tips so you can create your own data science curriculum. I hope to give others the tools to start their own educational journey. So they can start working towards a more passionate career in data science.
A Qucik Note
When I say “data science,” I’m talking about the collection of tools to convert data into action. These include machine learning, database technologies, statistics, programming and domain-specific technologies.
Top Resource From Where You Can Learn Data Science
There are many resource on internet from which you can learn data sceience but question araised it here which resources is good to learn data science quickly ?. Now i am going to introduce you top websites how you can access it free of cost.
Coursera.org is top e-learning website from which you can learn data science. The most popular courses on data science is IBM Data Science Professional Certificate . This course is provided by IBM Company. If you are beginner and don’t now how to learn data Science this course is best for you. This IBM Data Science Path includes the following courses
Now how you can access the course because this course is not avaliable for free ?. I am showing you a method. You can apply for this course with Financial Aid
Datacamp is also most popular website to learn data science using python or R programming language. Datacamp have also learning paths which you need to follow to become a data scientitst. But how you access this courses for free to learn data science? . So don’t worry i am showing a method which you may need to follow . First of all create a Microsoft account then you need to search for visual studio and visit his site . So you need to login and then search for benefits from where you can access datacamp websites for free of cost to learn data science.
Enjoy! Now don’t need to worry how to learn data science because at datacamp courses they start from zero means from beginning to advance. These two are most popular websites to learn data science . There are also many websites from which you can learn data science but these two are at the top.
A Curriculum Guideline to Learn Data Science
Python Programming for Data Science
Programming is a fundamental skill of data scientists. Familiarize yourself with the Python syntax. Understand how to run a python program in different ways. (Jupyter notebook vs command line vs IDE). It took me about a month to review the Python documents, the Python Hitchhiker Guide, and coding issues in CodeSignal. Tip: pay attention to common problem-solving techniques used by programmers (pronounced “algorithms”)
Statistics & Linear Algebra for Data Science
A prerequisite for machine learning and data analysis. If you already have a good understanding, take a week or two to review the key concepts. Concentrate on descriptive statistics. Being able to understand a set of data is a skill that is worth its weight in gold.
Numpy, Pandas and Matplotlib libraries for Data Science
Learn how to load, manipulate and visualize data. Mastering these libraries will be crucial for your personal projects. Quick suggestion: it is not necessary to memorize each method or function name, it comes with practice. If you forget it, search for it in Google. Access the tutorials of Pandas Docs, Numpy Docs and Matplotlib. There are better resources, but those are the ones I used.Remember that the only way to learn these libraries is to use them!
Machine learning for Data Science
Learn the theory and application of machine learning algorithms. Then apply the concepts you have learned to the real data that interests you. Most beginners start working with toy datasets from the UCI ML repository. Play with the data and follow the guided ML tutorials. The Scikit-learn documentation offers excellent tutorials on the application of common algorithms. I also found that this podcast is an excellent educational resource (and free) behind ML theory. You can listen to it during your trip or during your exercises.