Data science and machine learning have been the buzzword in the tech world for quite some time. The prospect of working with data, making clear and concise decisions, and a high payout career may seem lucrative to anyone. However, it’s not possible to become an expert in this field that easily. There are a lot of basics like statistics, programming, and analytics to cover before getting a hang of things. If all of these seem a bit too overwhelming, there are a few great foundation courses on data science and ML to get started from scratch. Check out which one best fits your needs. Why Should You Learn Data Science and ML? Let us ask you this, why shouldn’t you? Data science and ML holds the highest job prospect in the coming years. In fact, there are currently over 200,000 data science and ML-related jobs on LinkedIn alone. Industry leaders predict that the need for data scientists will increase by over 26% in the next 5 years. And the growth is expected to be exponential from thereon. While there are a lot of jobs at risk of becoming invalid due to automation and AI, the need for data science majors will forever be on the rise. And if the job security wasn’t enough to generate interest, the general median salary for a data science major in the USA is around 110,000 USD which roughly translates to 95,08,182 BDT per year. Did that grab your attention? Read along about how you can get started in your data science and ML journey. Read How to Be a Data Analyst from CSE or Non-CSE Backgrounds?  Top 9 Free Beginner’s Courses to Learn Data Science and ML  Data Science Specialization – Coursera This course is offered by Coursera in association with John Hopkins University. The free course is a mid-tier one designed for people who already have a grasp of the basics of statistics and R. The course will follow a detailed guide on using R to clean and sift data, manage projects and publish using Github, and data acquisition. It will also entail detailed regression analysis procedures using different regression models. The approximate course duration is about 11 months. Introduction to Machine Learning – Udacity Machine learning is almost as complicated as its name. It combines two significantly difficult disciplines – computer science and statistics to deliver a powerful predictive mechanism that makes up the base for modern data science. This intermediate-level course will introduce the students to the machine learning lens, data extraction process, and predictive algorithms. The approximate course duration is 10 weeks. Read Free Online Basic Programming Courses for Beginners Data Science Fundamentals – IBM Who better to learn data science from than the company that made the first computer? This beginner-level course is provided by IBM in association with Cognitive Class. The main aim of this course is to initiate the students with the very basics of data, its processes, life cycle, usage, and application. This course is part of a series of foundation courses that gradually progresses to intermediate and advanced levels. Students will also learn about different open-source data management tools. The approximate duration of the course is 10 weeks. Introduction to Data Science – Metis This course is a free introductory step to the data science boot camp offered by Metis. It is a small course that will be useful for beginners trying to get their head around data science. Students don't necessarily have to participate in the Bootcamp afterward, rather it’s a stepping stone to the world of data analytics. The 5-week-long course will see students learn about data cleaning, model creation, validation, and visualization. Read What is the Best Programming Language to Learn First? Machine Learning with Python – Coursera This Coursera course is supervised by IBM and is part of a series of machine learning levels. This is a beginner course that follows the integration of the commonly used programming language Python with ML. Students will learn about model evaluation, supervised learning, unsupervised learning, and different ML algorithms. The course will take approximately 22 hours to complete. Applied Data Science with Python Specialization – Coursera This applied data science course is fulfilled by the University of Michigan. This is an intermediate-level course with the main focus on the application end. Students wishing to get the basics first can check out the other 4 parts of this concurrent course. This specific course will focus on inferential statistical analysis and its implications, applied ML algorithms, and how to interpret results. The course will also focus on different data visualization techniques. The approximate course duration is 5 months. Read Game-based Programming: Best Coding Games for Children Data Quest Data Quest is an online platform that is all about data science and ML. But instead of the regular courses, the platform takes a more innovative approach to data science. Instead of having video lectures that guide the students first, the data quest starts with projects. While it may seem daunting at first, the interactive nature of the project and clear guidelines makes it easy for anyone to pick up the quirks. There are all sorts of resources available for free including paid plans. Data Science for Everyone – DataCamp This course is pretty much like its name. It’s a completely non-technical course focusing on the very basics of data science for absolute beginners. It starts by addressing what data science is and how it can be incorporated into modern jobs or how it benefits the big techs. It also gives an idea about how probability is related to computation and ML predictions. There aren’t any technicalities here so anyone can have a go-to to see whether data science is actually for them or not. Read How to Get a Job in Google from Bangladesh? Learn Data Science with R – Udemy The main basis of data science is made up of probability and machine learning. While ML mainly works with Python, data scientists need in-depth knowledge about R to get started with probability and regression model formation. This 10-part series from Udemy takes the students from the basics to the intermediate level of using R for data science. The course doesn’t connect the detailed dots with data management, but the students will learn much about sourcing and cleaning data for model applications. Final Words So far, we have shared 10 open source online courses for learning data science, data analytics and machine learning. Many people get attracted to data science just because of all the noise and prospects around it. But in reality, it's not everyone's cup of tea. But should that stop people from having a go at it? Absolutely not. Instead, these courses are a great starting point to see whether data science and ML match your passion and skill set and whether they can be a long-term career choice. Read Top 10 In-demand Tech Skills Freshers Should Learn in 2022

Data Analyst is one of the most prospective in-demand career options for 2022 and beyond. People from CSE and other backgrounds are choosing career options related to Big Data for sustainable futures. Stay with us to know the significance of Data Analytics and what the Data Analysts do. We will also highlight the steps to start a career as a Data Analyst. What Does a Data Analyst Do?  In recent years businesses across all sectors have made significant investments in big data, establishing analytics departments – particularly in telecommunications, insurance, advertising, financial services, healthcare, and technology. That rise is to continue for the foreseeable future, as businesses that have lagged behind in adopting big data analytics – such as education, government, and manufacturing – have committed to increasing their activities of big data analytics in the future. Read: Delivering Successful Business Presentation: What are the Ways and Secrets? Businesses gather data on every aspect of their operations and expenses, including operating expenditures, sales data, inventory and supply chain data, and much more. However, once they receive the data, they must translate it into information that can be used to drive their decision-making. Data analysts transform customer, product, performance, and cost data into actionable insights that may be used to inform strategic decision-making. They may assist organizations in strategizing product creation and predicting which audiences will be the most receptive, for example. To succeed as a Data Analyst, you must possess critical data analysis abilities and the ability to mine massive data sets for insights. Read $1 Salary Club: Why do CEOs take a one-dollar annual salary? Is It Possible to Be a Data Analyst from Non-CSE Background? It is often considered that pursuing a career in data science requires a formal background in computer science. Data scientists' definitions and job descriptions differ per firm. However, it is self-evident that a data scientist should be able to manage enormous amounts of unstructured data and derive insights. According to studies, data scientists come from a varied range of backgrounds. Sixty-seven percent of data scientists do not have a background in computer science. They come from a variety of areas, including sociology, math, statistics, commerce, fine arts, and humanities. Read: How to Become a Programmer: An Ultimate Guide Step by Step Guideline to Be A Data Analyst? Data Analytics Fundamental The first step should be to learn the fundamentals of data analysis as well as data analysis tools like advanced Microsoft Excel; several programming languages such as SQL, Python, and R; Spark and Hadoop for numeric; and Tableau, Matplotlib, or ggplot2 for creating unique visual representations that bring data to life. Data analysis courses can be an excellent method to master these core data skills, providing you with a solid foundation on all of these platforms as well as hands-on expertise with data management, analytical programming, programming languages, data cleansing, data visualization, and more. Read Wrong Career Path: Signs and What to Do? Work on Real Projects To be a Data Analyst, you'll need to learn the fundamental tools used by Data Analysts. After that, you may start putting your knowledge to use. Successful Data Analysts will need to stay current on the newest and best data analysis tools. They will need to be familiar with one or more of the top data analysis systems, such as Google Analytics and Adobe Analytics. Create practice projects that cover all aspects of data analysis, including investigating organizations and market prospects, determining the specifications of the data you need to acquire, acquiring and cleaning that data, and modeling it using custom-built algorithms. Read Personal Branding: How to Brand Yourself the Right Way Finally, make attractive visualizations of the insights you've gained from your work, or try putting them into a dashboard that enables people to query and interact with your information in a user-friendly manner. Practice Visualization Presenting Them To become a professional Data Analyst, you should begin by learning how to use applications such as Tableau, PowerBI, Bokeh, Plotly, or Infogram. Build your own visualizations from the ground up, figuring out the best method to let the data speak for itself. Even during this phase, Excel comes into play: although the fundamental idea of spreadsheets is simple – creating calculations or graphs by connecting the information in their columns – Excel has remained very helpful after more than 30 years and is nearly inevitable in the area of data science. Read: Future Resume: How to Project Yourself 5 or 10 Years from Now However, producing visualizations is just the beginning. Data analysts must also be able to convey their results using these representations. These communication skills may come easily to you, but if not, practice will help you improve. If necessary, begin small, presenting presentations to a single acquaintance, for example, before progressing to coworkers. Develop a Portfolio Choosing how to exhibit your data analysis abilities and expertise is one of the most crucial tasks to consider while determining how to become a Data Analyst. A professional portfolio is required, and to begin, you should post the code you've developed on GitHub to demonstrate your expertise and begin developing your professional portfolio. Joining an online data science community like Kaggle is another wonderful opportunity to demonstrate your involvement with the community, show your skills as an aspiring Data Analyst, and continue to expand both your experience and your outreach. Read Reciprocity Ring: How can it improve an organization and individual career? Finally, completing a well-executed project on your own might be an excellent method to exhibit your data analysis skills and impress prospective hiring managers. Choose anything that piques your curiosity, pose a question about it, then attempt to answer it using facts. Document your trip and present your findings—beautifully visualized—along with a comprehensive description of your method, emphasizing your technical abilities and creativity. Apply to Relevant Job With data expertise, you may gain a broad range of Data Analyst employment. All of the following positions, which might be entry-level or more senior data roles, rely significantly on data analytics. There are plenty of additional versions available—new uses for data analytics are always being created, and even the positions mentioned here will vary as data analysis becomes more common.  Read Paid vs Unpaid Internship: Pros and Cons According to consulting company Mckinsey & Co., such a highly dynamic area implies demand may surpass the predicted supply of data specialists by 50 or 60 percent, making Data Analyst roles even more difficult to fill. All of this is to imply that if you have Data Analyst abilities, you're already in a good position to pursue a Data Analyst profession. Conclusion That brings us to the end of our instruction on how to become a data analyst. You now have a clear, step-by-step strategy to obtain the essential abilities and break into the field. You can definitely acquire the principles of data analytics in six months if you spend 15 to 20 hours per week studying via an organized program, such as a Bootcamp. A degree in data analysis or a similar profession will take roughly four years to complete. It might also take considerably longer if you attempt to study on your own and compile your own materials. Read Poor Job Satisfaction: 10 Sure-fire Signs for Career Change

Value Base Academy has recently launched an Applied Data Science Programme to develop the technology sector.