Analytics Insight Presents the list of Top Free Online Courses in Statistics and Data Analysis

Would you like to understand data science statistics without undergoing a time-consuming and pricey class? There’s great news! Using solely free online resources, you may understand basic topics such as probability, Bayesian thinking, and statistical deep learning.

This article will show you the statistical thinking skills you’ll need for data science along with the top free online courses for Statistics and Data Analysis. It will give you a tremendous leg up on other budding data scientists who are attempting to get by without it. After all, after you’ve understood how to programme, it can be appealing to jump right into using machine learning programmes. It’s fine if you want to start with real-world projects at first. However, you should never, ever disregard statistics and probability concepts. It’s necessary if you want to advance as a data scientist.

Statistics Needed for Data Science

Statistics is a vast field with several applications in a variety of fields. The science of the collection, analysis, interpretation, presentation and organising of data is statistics, according to Encyclopaedia. As a result, it should come as no shock that data scientists require statistical knowledge.

Data analysis, for particular, necessitates at the very least descriptive statistics and probability theory. These ideas will assist you in making better company decisions based on data. Probability distributions, statistical significance, hypothesis testing, and regression are all important issues.

Artificial learning also necessitates knowledge of Bayesian thinking. The act of upgrading beliefs when new evidence is gathered is known as Bayesian thinking, and it’s at the heart of many machine learning frameworks. Conditional probability, priors and posteriors, and maximum probability are all important topics. Wouldn’t fret if those terms seem like jargon to you. When you get your hands grimy and actually learn, everything will sound familiar.

How to Learn Statistics for Data Science?

You’ve surely observed that “the self-starter route to learning X” frequently includes skipping classroom instruction in favour of “doing stuff.”

It’s no different when it comes to mastering statistics for data science.

In fact, we’ll be tackling important statistical ideas by programming them with coding! This will be a lot of fun, we promise. If you don’t have a formal math background, you’ll find that this method is far more natural than having a hard time figuring out difficult equations. It works by stimulating through each calculation’s logical phases. If you have a strong math background, this method will assist you in putting theory into practice while also providing some enjoyable programming difficulties.

You’ll be prepared to undertake harder machine learning issues and popular real-world data science applications after finishing these three levels. The three steps to studying the statistics and probability needed for data science are as follows:

Step 1: Core Statistics Concepts

It’s a good idea to start learning statistics for data science by examining how it will be applied.

Now let us look at some real-world studies or implementations that you might encounter as a data scientist:

1. Experimental design: Your firm is launching a new line of products, but it will only be available in brick-and-mortar locations. You’ll need to create an A/B test that accounts for geographic variances. You’ll also have to figure out just how many outlets you’ll need to test in order to get statistically relevant findings.

2. Regression modelling: Your enterprise needs to be able to forecast consumption for certain product lines in its outlets more accurately. Both understocking and overstocking are costly. You’re thinking of creating a set of regularized regression models.

3. Data transformation: You’re evaluating a number of machine learning model options. Several of them include assumptions about input data probability distributions, and you must be able to spot them so that you can either convert the data correctly or determine when the presumptions may be eased.

Step 2: Bayesian Thinking

The disagreement between Bayesians and frequentists is one of the philosophical arguments in statistics. While mastering statistics for data science, the Bayesian side is more important.

Frequentists, in an essence, solely employ probability to model sampling processes. This means that they only allocate probability to data that they’ve already gathered.

Step 3: Intro to Statistical Machine Learning

After you’ve grasped essential principles and Bayesian thinking, there’s no better way to learn statistics for data science than by experimenting with statistical machine learning models.

The sciences of statistics and machine learning are inextricably intertwined, and “statistical” machine learning is the predominant method of current machine learning.

In this stage, you’ll create a few machine learning models from the ground up. This will assist you in gaining a genuine knowledge of their dynamics.

Top Free Courses

1. Coursera (Duke University): Statistics with R Specialisation

• Time Period: 10 weeks
• Background knowledge: No prior programming expertise is necessary; just simple mathematics skills are required.

2.  Udacity (Stanford University): Intro to Statistics

• Time Period: 8 weeks
• Background knowledge: No prior experience is necessary; an introductory course is required

3.  Stanford University: Statistical Learning

• Time Period: 10 weeks
• Background knowledge: A basic understanding of statistics, linear algebra and computing are necessary.

4. Leada: Introduction to R

• Time Period: Self-Paced
• Background knowledge: No prior experience is necessary; an introductory course is required

5. Udacity (San Jose State University): Statistics: The Science of Decisions

• Time Period: Self-Paced; approximately 4 months
• Background knowledge: Basic proportions (fractions, decimals, and percentages), negative values, fundamental algebra (solving equations), and exponential and square roots.

6. Saylor: Introduction to Probability Theory

• Time Period: Self-Paced
• Background knowledge: Topics in single-variable and multivariate calculus, numerical analysis, and differential equations, or equivalents, must be completed.

7. EDX (Columbia University): Statistical Thinking for Data Science and Analytics

• Time Period: 5 weeks
• Background knowledge: No prior experience is necessary; an introductory course is required

8. EDX (University of Texas): Statistics Using R

• Time Period: 6 weeks
• Background knowledge: No prior experience is necessary; an introductory course is required

9. Caltech: Learning from Data

• Time Period: Self-Paced
• Background knowledge: No prior experience is necessary; an introductory course is required

Conclusion

We hope that we were able to provide you with the best free courses for Statistics and Data Analysis. They are ranked from 1 to 9 with short details which will help you pick your courses according to your convenience. So, hurry up and get yourself a course now!

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Analytics Insight Presents the Top 10 Big Data Statistics for You to Know in 2021.

The future is bright for companies that use Big Data and analytics in this cut-throat competitive market. People are generating more than 2.5 Qn bytes of real-time data due to globalization and digital transformation in the tech-driven era. IoT is also providing data through multiple smart devices, social media accounts, and search engines. The scope of Big Data is increasing at an increasing rate that leads to more job opportunities in the field of Data Science and other disruptive technology fields. Ample Big Data software tools are available to beginners as well as professionals for effective data management to generate interactive reports for meaningful in-depth business insights. Thus, reputed companies and start-ups have started adopting Big Data by investing millions of dollars. Let’s look at the top 10 Big Data statistics to predict the nearby future of this data-driven world.

Top 10 Big Data Statistics You Must Know in 2021

According to Statista, the market survey report showed that the total amount of data being consumed globally was forecasted to increase rapidly to 64.2 zettabytes in 2020 and 79 zettabytes in 2021 while it is projected to grow to over 180 zettabytes up to 2025. It also reported that the installed base of storage capacity will increase at a compound annual growth rate of 19.2% from 2020 to 2025.

The Big Data and business analytics revenue report from Statista showed the forecast of the Big Data market that it will grow to US$274.3 billion by 2022 with a five-year CAGR of 13.2%. The global cloud data center IP traffic will reach almost 19.5 zettabytes in 2021.

BARC, reported that organizations are reaping the benefits of Big Data— 69% chance of better strategic decisions, 54% chance of enhanced operational process control, 52% for a better understanding of consumers as well as 47% for effective cost reduction. The organizations that are reaping the benefits of Big Data reported an average 8% increase in revenues while there is a 10% reduction in costs.

Statista, forecasted that the Big Data market segment will grow up to US$103 billion by 2027 with a share of 45% from the software segment. The market is expected to receive annual revenue of US$274 billion in the next year, 2022.

Forbes, predicted that more than 150 zettabytes or 150 trillion gigabytes of real-time data will need analysis by 2025. Multiple companies dealing with structured data need different things than the companies using unstructured data. Forbes found that over 95% of companies require some help to manage the multiple sets of unstructured data while 40% of companies claimed that they need to deal with Big Data more frequently.

StrategyMRC, predicted that the Hadoop and Big Data Market will experience substantial growth from US$17.1 billion in 2017 to US$99.31 billion in 2022 with a 28.5% CAGR. The Big Data market is expected to jump US$30 billion in value in 2021 and 2022.

It is predicted by Statista, that the global Big Data revenue will experience a major shift in using Big Data in services, hardware, and software. In 2021, there is 24% in services, 16% in hardware, and 24% in software while there will be 33% in services, 24% in hardware as well as a whopping 46% in software use in 2027.

According to Wikibon, the Big Data and analytics, and application database solutions are expected to grow from US$6.4 billion in 2017 to US$12 billion by 2027 with a 6% CAGR, within a span of ten years. The demand for open-source platforms in the Big Data ecosystem such as Hadoop, Kafka, Spark, and TensorFlow can decline due to its direct address to Artificial Intelligence, machine learning, deep learning, or Data Science. But the hybrid deployment of data analytics platforms such as Hadoop, NoSQL, in-memory, streaming, and many more databases will experience a growth in market share for data lake and data fabric solutions.

Sigma, had a market survey to show how many business leaders are keen to adopt Big Data and analytics in their business. The result showed that 39% were not sure about the data-driven culture in organizations, 46% admitted that the lack of domain expertise creates a challenge for delivering relevant data models.

According to ReedSmith, the outbreak of the coronavirus pandemic has increased the rate of Big Data breaches and cyberattacks like scams, phishing, and ransomware to above 400%. The pandemic has forced people to use smart devices more than ever for online transactions and other purposes at home. Thus, there is a data explosion in the digital world that has created ample opportunities for malicious hackers.

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They call it “big data” for a reason–it’s really, really big. But getting your head wrapped around the growth of information digitization is not easy. That’s why we carefully curated these 10 mind-blowing facts about today’s data-geist, and how it’s projected to grow in the future.

1. The Global Datasphere will grow from 33 Zettabytes (ZB) in 2018 to 175 ZB by 2025, a 26% annual compound growth rate (CAGR), per IDC‘s DataAge 2025 report. However, only about 9ZB of that data will actually be stored,  up from about 0.9ZB in 2015. Only about one-third of the data that’s stored will actually be used, the analyst group says.

2. The annual capacity of shipped HDDs, SSDs, and LTO tape drives is projected to amount to about 1,300 exabytes in 2020, and will reach 4,500 exabytes by 2025, with HDDs accounting for the lion’s share of that capacity, according to Coughlin Associates. Per IDC, HDDs will account for more than 80% of enterprise storage needs by 2025, with legacy SSDs accounting for about 15% and newer NVMe-NAND solid state devices accounting for less than 5%.

3. HDD shipments peaked in 2010 with 651.3 million units, falling to 316.3 million by 2019, according to Statista. However, the number of HDD units shipped is expected to begin growing again in the next few years, as storage demands increase, according to several sources. (Clearly, the size of HDDs has increased substantially, enabling organizations to store more bytes on smaller number of physical units.)

4. Data storage on endpoint devices is projected to plummet by 2024 (despite the advent of super-fast 5G networks), as organizations move data storage to in-house and cloud data centers. In fact, the shift from using endpoints like cell phones, PCs, and IoT devices to store data to using core data centers completely reverses the dynamic from 2015, when most data was stored on endpoints and enterprise data storage was relatively small, according to IDC’s DataAge 2025 report.

5. More than 22 ZB of storage capacity will need to ship from 2018 to 2025 to keep up with storage demands, according to IDC’s DataAge 2025 report. About 59% of that capacity will come from HDD deliveries. The fraction stored on SSDs, including NVMe, will grow but it won’t put much of a dent in growth of HDD storage.

6. Every minute of every day, consumers spend $1 million online, make 1.4 video and voice calls, share 150,000 messages on Facebook, and stream 404,000 hours of video on Netflix, according to Domo’s eighth-annual Data Never Sleeps graphic.

7. More data is created every hour today than in an entire year just 20 years ago, according to the Seagate Rethink Data Survey by IDC, which was released in January 2020.

8. The public cloud will store more data than enterprise data centers by 2021, according to IDC’s DataAge 2025 report.  (This  figure was  before COVID-19,  which  has  accelerated  many  organizations’  cloud-migration  plans.)

9. AWS currently has 77 Availability Zones (AZs) around the world, with three more planned. According to 2014 estimates by TPM, that likely means that AWS today owns and operates anywhere from 150 to 220 data centers around the world (assuming an average of two to three data centers per AZ). If each data center contains 50,000 to 80,000 servers (which was the case in 2014, per AWS engineer James Hamilton’s re:Invent presentation), that means AWS runs 7.5 million to 17.6 million servers. Bezos only knows how much data AWS stores.

10. Each connected person will have at least one data interaction every 18 seconds. Many of these interactions are because of the billions of IoT devices connected across the globe, which are expected to create over 90ZB of data in 2025. Over the next five years, approximately one billion more people will begin to interact with data every day, representing 75% of the earth’s population, per IDC.

Big data may not be the headline maker that it was in 2015. But the underlying drivers that put big data on the map back then are still in play, and in fact are accelerating in some cases. Perhaps the phenomenon needs a new name. 

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Methodologies of data science, in some way, come from machine learning, and both are often associated with mathematics, statistics, algorithms and data wrangling. Data scientists make data models that need to run in production environments. And most DevOps practices are germane to production-oriented data science applications, but these practices are typically unheeded in data science training.

Many organizations may not be ready to invest in data science platforms, or maybe they have small data science teams for only basic operations. In this case, companies must apply DevOps best practices to data science teams instead of picking and orchestrating a platform. To do so, several agile and DevOps paradigms being utilized for software development teams can be employed to data science workflows with some significant tunings.

DevOps encompasses infrastructure provisioning, configuration management, continuous integration and deployment, experimenting and monitoring. The teams in DevOps have been closely working with the development teams to manage the applications’ lifecycle efficiently.

Applying DevOps to Data Science

Data science teams add extra responsibilities to DevOps. And data engineering, a niche domain which deals with multifaceted pipelines to transform the data, demands the close collaboration of data science teams with DevOps. Additionally, operators are also anticipated to supply highly available clusters of Apache Hadoop, Apache Kafka, Apache Spark and Apache Airflow to address data extraction and transformation.

Data scientists discover transformed data to explore insights and correlations. They embrace a diverse set of tools like Jupyter Notebooks, Pandas, Tableau and Power BI to visualize data. So, the DevOps teams are expected to support data scientists by creating environments for data exploration and visualization.

Begin with Delivering Assistance to Data Scientists

Data scientists, similar to application developers, are most involved in solving problems, interested in configuring their tools, and often have less curiosity in configuring infrastructure. But they may not have the same experience and background, as software developers have, to fully configure their development workflows. This provides an opportunity to DevOps engineers to treat data scientists as customers, assist them to define their requirements, and take ownership in delivering solutions.

A DevOps engineer can also help in selecting and standardizing a development environment. This can be performed traditionally on a computing device or on a virtualized desktop. Also, imitating their applications and configurations to the development ecosystem is significantly the first step for DevOps engineers when working with data scientists. Afterward, they should review where data scientists store their code, how the code is versioned, and how code is packaged for implementation.

Most of the data scientists are relatively new to using version control tools like Git, and maybe using a code repository but have not automated any integrations. So, deploying continuous integration is an important second place for DevOps engineers to lend a hand to data scientists, as it creates standards and confiscates some of the manual work in testing new algorithms.

Moreover, developing machine learning models is essentially different from traditional application development. When a fully-trained machine learning model is available, DevOps teams are expected to host the model in a scalable environment. They could also take benefit of orchestration engines like Apache Mesos or Kubernetes to scale the model implementation.

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