best recommendations for data science online courses for beginners

7 Best Recommendations For Data Science Online Courses That Beginners Can Consider Taking

We are now entering the era of information technology. And one of the consequences will be a data explosion. It is now commonplace for a person, business, or organization to use data as a tool to make strategic decisions.

Everyone can become a reliable data scientist. Even someone who has no experience in this field can still become a data scientist. Taking online data science courses for beginners is the best path to a career in data science.

Businesses or organizations need people with data science skills to help them process and analyze data so that they get enough insights to determine their business development recommendations.

This condition causes the need for data scientists in the field to continue to increase. But on the other hand, the availability of reliable data scientists has not been able to meet the demand.

This same condition has also occurred in the world of computer science a few years ago.

This imbalance between the availability of data scientists and their demand creates a great opportunity for a career in this field. Now is the perfect time to learn data science.

Are you a beginner? Make sure you already have the right understanding of data science before you go any deeper.

Data science in simple words.

Data science is a knowledge to analyze and process data.

Over time, whether it’s a business, organization, or community, there must be a lot of data on what they’ve done.

Data will only become data, it can even become garbage if the data is not analyzed and processed properly. With the ability to “read” good data, data will become a powerful tool for companies to make improvements.

Especially in data science, the data analysis process will use methods, algorithms, and scientific systems, so that data can be extracted and processed appropriately into more accurate data.

Data science can be used to process structured data as well as unstructured data.

Do you want to become a data scientist? You will often come into contact with data mining, machine learning, and big data.

In simple terms, data science is the knowledge of processing big data so that it can provide useful recommendations using a data-driven approach, combining statistics and computing.

A data scientist is someone who will play a role in processing and issuing these recommendations. They need to have both technical and non-technical skills.

In general, a data scientist will do his job starting from extracting data and interpreting it. They will involve machine learning and statistics in their work.

A good data scientist will continue to pay attention to the mindset of humans in general and make it a foothold in the data processing.

How do we start learning data science?

To learn data science, of course, we must first understand its important essences.

For a beginner who wants to learn about data science, it is recommended to learn the following three things:

  1. Business analysis.
  2. SQL, Python, and R programming languages.
  3. Data visualization.

Of the three subjects, it is hoped that anyone will be able to start their career in the data science field.

The following is an explanation of each of the points that we must master when we want to enter the world of data science:

Analyze the business.This stage is more about collecting data and identifying the business that we are going to analyze. Here we have to find problems and opportunities that exist in business.
Learn a programming language.There are times when we will be dealing with big data. Of course, to analyze or process large amounts of data, it will be easier if we use “tools.” With computing, this programming language will help us extract data and perform data queries.
Data visualization.From the data that you have obtained and analyzed, we need to visualize it to inform those who are going to consume it. Good visualization will make it easier for someone to read and understand data. To determine strategic steps, a data scientist must be able to deliver and explain this important data to stakeholders and business owners.

Taking data science online courses is one of the best ways to become a reliable data scientist.

Online is just a medium for how we will study data science.

In the past, if we wanted to learn something, of course, we looked for a course where we could come directly to that place. Now it’s different. With the advancement of technology called the internet, we can do everything online, including learning data science.

Actually what needs to be considered as not a problem online or offline. But more to what we will learn to become data scientists.

There are many stories where someone takes a long time to master data science because they initially made a mistake in determining what they were studying.

Not only longer time, but sometimes they also spend more money to learn data science.

The advantage of taking an online data science course is its flexibility. We don’t need to go to a certain place, we just need to be in front of the computer, prepare notes, maybe a cup of coffee, and we are ready to learn data science. Everyone will learn more optimally and get better learning outcomes if they can learn at their own pace.

Online course providers such as Udacity even allow students to determine their own course schedule. Of course, this will be an advantage for people who will study data science.

Best online data science courses for beginners.

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If above we already know the meaning of data science simply, then now is the time to start learning data science.

Especially for beginners, of course, they have to follow the topics of online data science courses from scratch so that they will get maximum learning results.

Indeed, if only to find out what data science is, we can get the information by searching for it on Google or looking for the information in the form of videos on YouTube.

But if you want to really dive into data science, it is highly recommended to take an online course on it.

Not all data science online courses for beginners are paid. You can follow some of them for free.

The data science online courses for beginners that will be written in this section have certainly gone through several stages of selection, why in the end these courses are considered appropriate to bring someone to become a reliable data scientist.

We got a list of these courses from Udacity. Through their program which they named the Nanodegree program, Udacity has long been a trusted course provider organization, especially for technology-related courses.

And some of the advantages if you enroll in courses at Udacity, here you will learn directly from the experts, and you can take part in real-world projects according to the topic of the course you are taking.

Udacity also provides special services, namely career assistance. So here, besides learning to become a data scientist, you will be helped to determine your career path in the data science field.

Here are data science online courses for beginners that Udacity provides:

1. SQL

Why do you have to enroll in this course?

For data scientist positions, recruiters will look for professionals with SQL skills.

Currently, SQL is the skill most needed for jobs related to data science, even higher when compared to other programming languages ​​such as Python, Java, and JavaScript.

LinkedIn also mentions that there are more than 440,000 jobs in the world related to SQL.

The advantage of taking this course is that SQL is also suitable for other jobs besides data scientists such as product analysts, data analysts, business analysts, product managers, software engineers, and many more.

By taking this SQL course you will learn when to use SQL to provide data-backed insights into complex business strategies.

Here later you will learn more about how to leverage the power of SQL in generating insights from relational databases, as well as learn about situational differences using relational databases vs non-relational databases.

And last but not least, in this Udacity SQL course, you will gain experience working on real-world projects.

  • Course provider: Udacity.
  • Estimated completion time: 2 months.
  • Skills covered: SQL, PostgreSQL, JOINs, Subqueries, Window Functions, Partitions, Data Cleaning, DDL, DML, Relational and Non-Relational Databases.

2. Programming for Data Science with Phyton

The fact is that there are more than 59% of companies, now planning for their staff to have data analysis skills.

In this Udacity Nanodegree program course, you will learn the programming languages ​​that are both the most important and the most widely used by data scientists. Not only Python, but here you will also learn about SQL, Terminals, and Git, directly from a mentor who is an expert in that programming language.

Python, SQL, and Git are foundational tools for data science programming.

In this course, you will also learn about special data libraries for Python such as Pandas and Numpy. At the same time, you will learn how to use Git and Terminal to share your work, and also you will learn about version control.

Because what you will learn in this course is fundamental programming languages, it is hoped that you will have qualified skills and you will be ready to pursue your career in data science even better.

To take this course, no special prerequisite knowledge about data science is required. So whoever you are, if you want to master data science, you can immediately enroll in this course.

  • Course provider: Udacity.
  • Estimated completion time: 3 months.
  • Skills covered: Python, Numpy & Pandas, SQL, Git & GitHub.

3. Programming for Data Science with R

The R programming language is another option of a fundamental language that is often used by data scientists besides Python, SQL, and Git.

Similar to course number 2 above, this course is also suitable for those of you who are interested in entering the world of data science for the first time.

The number 2 course with this one has the same dataset and skills. However, the approaches of the two courses are different. This course puts more emphasis on the R programming language while the previous course places more emphasis on using Python as a programming language tool. Of course, what will be the last to be delivered will also be different.

The fundamental concept between the two courses is the same. SQL, the command line, and the Git curriculum are the same. Only the programming language used is different.

Because here learning a different programming language, the course programs and projects in the course are also different. You can see the program and the project in more detail via this link.

This course includes an introductory program to data science. This course is also not designed for a specific job. So if you graduate from this course, it is hoped that you will have reliable programming skills such as R, SQL, Terminal, and Git, which of course you can use to complete a lot of work-related data analysis and data science.

There are no special prerequisites for you to enroll in this course.

  • Course provider: Udacity.
  • Estimated completion time: 3 months.
  • Skills covered: R, SQL, Git.

4. Data Visualization

Data visualization is indeed a job that is not directly related to data science work. However, this data visualization is important because this work is a continuation of what has been done previously in the field of data science.

Data that has been collected by data scientists must be well visualized so that the data can be better consumed by stakeholders or other parties who will use the data.

With good data visualization, data can communicate information more accurately and efficiently.

Businesses are starting to see this data visualization as a new necessity.

And it turns out, the fact about data analysis and communication is a skill that is constantly being needed by companies. LinkedIn has done some research, and the results show that data presentation is one of the 10 most in-demand skills.

This Udacity data visualization course will teach you how to visualize data and use it to communicate data-driven recommendations more effectively.

Similar to other courses at Udacity, you will be accompanied by mentors who are experienced in their fields. And in this course, you will be taught how to combine hard skills in the field of data analysis and visualization with soft skills in the fields of presentation, storytelling, and communication to create effective presentations.

This data visualization capability is not only suitable for jobs related to data science. However, you can apply this ability to other fields such as business, marketing, data analytics, executive leadership, and many more.

If you are a business leader or a data professional, then this data visualization course is for you. Because by studying in this course, you will have the ability to present business objectives based on data, which are packaged in the form of a story.

And if you are a data scientist, data analyst, or machine learning engineer, then this course will teach you how you can convey your findings more effectively. With good data visualization, you will be able to influence related parties in your business by showing and communicating important data.

So that you are more sure about enrolling in this course, you can read the review of this Udacity Data Visualization course beforehand.

Data visualization is important in data science.

  • Course provider: Udacity.
  • Estimated completion time: 4 months.
  • Skills covered: Data Visualization, Tableau, Dashboards, Data Storytelling.

5. Business Analytics

This course will build your fundamental skills in the field of data.

It is called fundamental because in this course you will be introduced to what data is, at the same time you will also learn how people use data to improve their business performance.

Data has become very important for a business. And by doing data analysis and data processing, businesses can provide better value for their organization and even for their customers.

This course is a great introduction to the importance of data and analysis. In this course, you will be taught many skills that you can apply in many types of work, but of course, the skills you will learn here will be the main foundation for a successful career in data science and data analysis.

That’s why this course is the most recommended as the first step for anyone looking to become a reliable data scientist or data analyst.

Not only will you be taught how you can analyze your business well, but here you will also be taught how you build models with Excel, query databases using SQL, to how to create effective data visualizations using Tableau.

To further ensure that you are suitable for enrolling in this course, you can read this Udacity Business Analytics course review here.

This course can be your starting point to enter the data science industry.

  • Course provider: Udacity.
  • Estimated completion time: 3 months.
  • Skills covered: Excel & Spreadsheets, SQL, Data Visualization, Data Dashboards.

6. Predictive Analytics for Business

Business analysis is one of the triggers where data science plays a role in providing strategic recommendations in a business.

And the fact is The Global Business Analytics Market is expected to hit $ 71.1 billion by 2022.

Unlike the previous business analysis courses, this course will focus more on using predictive analysis skills to assist businesses in making decisions.

This course has very little to do with technical stuff like coding. You will later use software like Alteryx and Tableau more often than using programming languages.

In this course, you will focus more on learning how to solve problems with advanced analytics, data wrangling, classification models, A / B testing, time series forecasting, as well as segmentation and clustering.

By completing this course, it is hoped that you can become someone who can direct the business to make even more precise strategic decisions.

Not many people realize that this business prediction analysis will continue to increase shortly.

It would be better if you could master the skills of this business prediction earlier.

Be the first, because this opportunity will have a good effect on your career path.

  • Course provider: Udacity.
  • Estimated completion time: 160 hours.
  • Skills covered: Data Wrangling, Classification, A/B Testing, Forecasting, Segmentation.

7. Data Analysis and Visualization with Power BI

Power BI is a very reliable tool for performing data pre-processing, visualization, and analysis.

By mastering Power BI, it will be a plus when compared to data scientists in general.

In fact, according to ZipRecruiter, a data scientist who masters Power BI has a better average annual salary in the US which can reach $94,627 per year.

Through this online course, we will learn to use Power BI for data preparation and modeling, create data visualizations, and analyze data more advanced.

At each stage of the course, we will have the opportunity to work on real-world projects, which will be an advantage because we get to know how a data scientist works using Power BI.

Take this online course, and you will have a competency that you can offer more than other data scientists.

  • Course provider: Udacity.
  • Estimated completion time: 3 months.
  • Skills covered: Power BI, Data Visualizations, Data Modeling, Extract-Transform-Load, Data Analytics Expressions.

Conclusion.

Data science is an interdisciplinary field capable of extracting knowledge and insights from structured or unstructured data using scientific methods, processes, algorithms, and systems.

Data science will always have a close relationship with data mining, data learning, and big data.

With data science, big data or large amounts of complex data can be processed into meaningful information.

In many organizations, businesses, or communities, data science will combine statistics and computing to interpret data so that the data can be used to make strategic decisions.

This condition then makes the demand for data scientists continue to increase.

A data scientist is someone who will play a role in extracting the data and interpreting the data. They will work from collecting, cleaning, and managing data using approaches such as statistics and machine learning, without compromising how humans should make decisions.

Are you someone who wants to be a data scientist?

Now is a good time to learn data science. A little added encouragement for you, seeing the importance of turning data into meaningful information, companies are now willing to pay data scientists with great value.

You don’t need to worry if you don’t have any previous experience in data science. Yes, all about data science can be learned by taking an online course. And you can even follow some of them for free.

We see Udacity as one of the best places (websites) to learn data science. They provide online courses both free and paid.

Here are recommendations for data science online courses that you can take for free (you can click the link below to see more details about the free online data science courses):

And if you are interested in learning more about data science, then you can consider taking the paid online courses mentioned in the section above.

You can also see from the list of online courses above, learning data science cannot leave programming. But from the recommendations of the online courses you can find out which programming languages ​​are proven to be good, so you don’t need to learn all programming languages, just learn those that are proven to be good for data science.

The time it takes to learn data science varies. It also depends on what topic about data science you want to study. You can see for yourself from the list of recommendations for online courses above, some of them take about 3 months, some are less and some take longer.

It’s best not to get hung up on how long it will take to learn data science. But focus on the goal that you can get a bright future by mastering data science.

What you need to do now is be committed and passionate about learning data science.

Taking data science online courses is your path to becoming a skilled data scientist.


References:
wikipedia.org ; linkedin.com ; investopedia.com ; datasciencedegree.wisconsin.edu

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