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How Can I Learn Data Science For Free?

You may try data science if you have a passion for research, coding, mathematics and computers. You can discover a lot of applications of data science in the real world and the steps you need to take to make a career out of this field. In this article, we will find out how you can learn data science for free. Read on to find out more. 

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With data science, professionals can use algorithms, processes and procedures to analyze large-scale data, reveal hidden patterns, get insights and make informed decisions. Data sciences use machine learning algorithms to collect, filter, organize and learn from data, which can be structured or unstructured. 

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Data is growing fast and uses a lot of applications in many industries. Therefore, this field offers a wide range of job opportunities, including research and computing. After reading this article, you will know pretty well how you can use data science in the real world, what skills you need and how you can do it for free. 

Now, let’s find out how you can learn data science for free. 

Step #1. Python Programming

First of all, you may want to start with Python, a user-friendly programming language for beginners. According to the Kaggle survey, about all of the data, scientists use Python on a regular basis. 

So, you may want to take your time to be familiar with different Python functions. The good news is that Python is easy to learn with practice. Even if you have no coding experience, you can learn Python in a few weeks. 

The primary topics that you may want to focus on include basic syntaxes, data collection, control flow, lambda functions, and loop & iterations, to name a few. 

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Step #2 Working with Data and Manipulation

Next, you need to learn how to work with data and perform manipulations. In the beginning, you will receive raw data that you have to manipulate. For this purpose, you need to use a Python library called Pandas. 

The good news is that this library allows you to use several functionalities to facilitate data analysis. Online tutorials can help you get the hang of it. 

Step #3. Working with Arrays

Next on the list is learning to work with arrays. With this library, you can work on arrays in an efficient manner. While learning data science, you may have to work with multi-dimensional arrays.

With NumPy, you can enhance the computing speed and use the memory efficiently. Moreover, NumPy allows you to perform a lot of mathematical functions. Moreover, Numpy works with other packages too. For instance, it supports scikit-learn, Matplotlib and Pandas.  

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Step #4. Statistics for Data Science

In any data science project, you will use statistics. In this case, you may find descriptive statistics useful enough to understand data and get a summary. If you want to extract insights, you may go for inferential statistics. 

For instance, you may analyze real estate data to get the ratings of nearby schools and the impact it may have on real estate prices. Besides, predictive model statistics can help you measure the model’s performance. 

Step #5. Learn SQL

SQL carries a lot of importance in the world of data science, but most data science professionals don’t pay much heed to it. As a matter of fact, this database language is quite popular among data scientists. 

Generally, the data is stored in a structured data store. And you need to learn SQL to access and work on the data. If you have no background in coding, you may want to improve your SQL skills. 

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In the same way, even if you are familiar with SQL, you need to practice a lot to strengthen your grip on the SQL concepts. You may get data in the form of different tables that you can manipulate to create tables that can provide answers to your questions. 

Step #6. Data Analysis and Feature Engineering

Exploratory data analysis is the ultimate step if you want to get the hang of the basic concepts. Regardless of your data science project, you will spend most of your time on data analysis projects. 

If you want to improve these skills, the only way is to practice as much as you can. In the same way, you need to practice a lot to improve your data analysis and data engineering skills. As a matter of fact, practice is the best approach to learning things faster. 

Feature engineering is the next step in the process of data analysis. Data is not perfect and may have a lot of issues that you need to deal with. In other words, you need to work on the data to turn it into a format that you can apply models and algorithms to. 

Therefore, you need to apply specific transformation techniques. If you are looking for the most commonly used engineering techniques, you can try binning, log transformation, on-hot encoding, and scaling, to name a few. 

In other words, if you practice with the Kaggle dataset, you can improve your understanding of engineering. For this purpose, you can head to discuss forums where you can learn fresh techniques in the world of feature engineering. 

Keep in mind that there is no right or wrong way of doing feature engineering. It all boils down to your creativity and the ability to innovate. This is how you can improve your results. 

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Long story short, you can follow these steps to learn data science for free. All you need to do is follow the steps, be determined, and practice as much as you can on a regular basis.

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