AI News, Free Data Science Resources for Beginners

Free Data Science Resources for Beginners

In this guide, we’ll share 65 free data science resources that we’ve hand-picked and annotated for beginners.

Foundational skills form the basis of true understanding, which will in turn allow you to discover novel solutions, build more accurate models, and make better decisions.

strong statistics foundation helps you fully understand machine learning, conditional probability, A/B testing, and many other core skills. It also helps you 'think like a data scientist,' which include spotting biases, efficiently iterating on predictive models, and knowing how to extract insights from data.

Plus, learning the common probability distributions (especially Gaussian, Binomial, Uniform, Exponential, Poisson) is critical for implementing many real-world applications, such as multi-armed bandits, market-basket analyses, and anomaly detection programs.

Core technical skills include collecting, cleaning, managing, and visualizing data, plus the big umbrella of applied machine learning.

There are 4 common ways to collect data: API Resources: Web Scraping Resources: SQL is the lingua franca for database management and querying, and you should be able to write complex queries.

Data visualization is important for exploratory analysis and for communicating your insights, and no list of data science resources would be complete without this topic.

To some people, machine learning is synonymous with data science, but we consider it a separate field that heavily overlaps with data science.

Business skills and soft skills are sometimes overlooked in data science curricula, but they are supremely important, and employers will look out for them.

As machine learning libraries mature and algorithms become easier to use 'out-of-the-box,' businesses will value people who can work with data and work with people.

You'll naturally become a better creative thinker as you gain more experience, but the following resources can help jumpstart your problem-solving and innovation skills.

They power many amazing websites and apps, including Amazon, Yelp, Netflix, and Spotify. In a nutshell, recommendation systems find other users who have similar tastes to you to make better recommendations for you.

While much of machine learning deals with 'cross-sectional data' (data without regard to differences in time), there are also models specifically designed to handle time series.

Practice projects have two main purposes: By nature, projects are personal undertakings, and you should pick topics you're interested in. Here are a few places to find project ideas: And that's a wrap!

The best data science learning resources out there and my journey into data science-

So, in this post I will be sharing some of my tips and the best resources out there which I used to get started when I was a complete stranger to this buzz word around 2 years back.

So this is literally the best time for anyone who is into this field or who aspires to become a data professional and wants to learn and get started to increase his/her employability chances, or say wants to learn it to experience something new or to stay updated and with the present trends, or who is deciding to change his professional field etc , this post will give you some of the handpicked, best possible resources out there to learn and get started with data science or simply improve and hone your data science, statistics and machine learning skills.

When I started my journey and got interested in this subject during the early days of my sophomore year at collage, I approached a senior who was a good friend of mine from my collage who was recently placed in a reputed Fintech firm as a Data scientist.

I too started with doing courses on descriptive and inferential statistics in R from DataCamp , joining various data science related groups on facebook, linkedIn, following and connecting with the data science professionals, influencers on github etc.

Exploratory data analysis in R -Next thing after learning statistics and probability theory is to get started with exploring the data and gathering information and extracting knowledge out of it with the help of the statistical and probabilistic techniques learned by doing the above courses.

Now for further list of courses you guys can search for amazing courses here courses on statistics and probability This is an amazing cheat sheet on probability and statistics formulas which I keep handy and use very often.

Next what I did was learned how to visualize data and make plots, which is really really important for any data analyst or scientist for exploring the data nicely and sharing their results.

Now to learn data visualization I didn’t do any course specifically rather, explored Youtube, google, downloaded various cheatsteets, used Kaggle to search for examples etc.

It can create versatile, data-driven graphics, and connect the full power of the entire Python data-science stack to rich, interactive visualizations.

I now knew how to extract knowledge and insights from data using statistical concepts, data visualization, uni-variate, bi-variate and multi-variate data analysis etc.

I believe you cannot find any other best course to get started with understanding the core mathematical concepts behind major supervised and unsupervised learning techniques.

2.Machine learning with R is a fantastic course track, which is basically all the machine learning fundamentals at one place, if you opt this amazing course then you don’t need to wander at different places and waste you time and energy being confused about what to do.

You can do projects, start doing some research work, compete on Kaggle competitions, start build applications, start blogging, learn uses of machine learning and data science for some specific fields such Deep learning, natural language processing ,computer vision, economics and finance, financial econometrics, time series forecasting etc.

The courses which are the best to get started with Deep learning and other applications of data science and machine learning are— Now if you are willing to spend some extra time and energy to learn the compete fundamentals in a single track course which teaches you the complete fundamentals from the very basics to the advanced level of data science, then you can definitely opt for these DataCamp track courses- 1.

I actually completed the Data scientist with R track course which included all the different courses which I have included in the different sections of this post into 1 single track course.

So it’s basically your choice , if you want to learn a specific topic then go for individual courses, or otherwise if you are a beginner and want to learn from the scratch and basics, then do go for these track courses which will include all the relevant and important topics and courses into 1 single track course and will help you build the right set of skills in a good incremental manner.

am writing articles, blogging, sharing my knowledge and resources which truly helped me become what I am today with you all, like this post which you read today.

A Visual Guide to Analytics, Data Science, Business Intelligence, Machine Learning, and AI

To clarify the relationships among the fields of business and data analytics, data science, business intelligence, machine learning, and artificial intelligence, the team at365 Data Sciencebuilt an Euler diagram.

Throughout this post, we'll explore how the diagram was created and how it illuminates these complex relationships with the help of different colors, a timeline, and example use cases.

Of the six initial business activities, you’ll need data to create: The other two items are experience driven: Neither of these two require data to be useful.

Business case studies are examinations of past activities carried out in the real world (similar to those in a history book) and qualitative analytics relies on professional knowledge to assist in future planning.

Forecasting, though, is a future-oriented activity, so it sits to the right of the black line, but not too much— it still belongs to the field of business and remains in the area where business and data intersect.

Data science (depicted here as a green rectangle), incorporates a portion of data analytics, mostly the part that uses complex mathematical, statistical, and programming tools.

The full intersection of data analytics and business analytics lies inside the data science section, including our previously discussed terms.

So, what is an example of a data science activity that does not fall under business analytics?Well, 'optimization of drilling' within the oil and gas industry, while related to business, it is not a part of business decision making, per se.

In addition, informed strategic and tactical business assessments based on visual reports and dashboards are made by end users like general managers.

Machine learning is about creating and implementing algorithms that let machines receive data and use this data to analyze patterns, make predictions, and give recommendations on their own.

Past fraudulent activity data can be fed to the machine, which will find patterns that the human brain is incapable of recognizing, all in real time— an approach that has helped financial organizations prevent numerous criminal acts.

Speech and image recognition are widely discussed right now, but there is debate over whether they belong under the umbrella of data science, data analytics, both, or neither.

AI itself has been interpreted in quite a philosophical manner at times and although we have only achieved AI through machine learning, there is a part of the field that sits outside of this realm.For the sake of completeness, an example that is AIbut not machine learningis symbolic reasoning.

Any part of what has been discussed could be defined as advanced, so, to keep things fair, advanced analytics shall encompass our entire set of fields.

The Big List of DS/ML Interview Resources

Through this exciting and somewhat (at times, very) painful process, I’ve accumulated a plethora of useful resources that helped me prepare for and eventually pass data science interviews.

Long story short, I’ve decided to sort through all my bookmarks and notes in order to deliver a comprehensive list of data science resources.

It’s worth noting that many of these resources are naturally going to geared towards entry-level and intern data science positions, as that’s where my expertise lies.

In my experience, this isn’t the case 100% of the time, but chances are you’ll be asked to work through something similar to an easy or medium question on LeetCode or HackerRank.

Once the interviewer knows that you can think-through problems and code effectively, chances are that you’ll move onto some more data science specific applications.

I’ve found that the difficulty level of these questions can vary a good bit, ranging from being painfully easy to requiring complex joins and obscure functions.

As positions get more experienced, I suspect this happens less and less as traditional statistical questions begin to take the more practical form of A/B testing scenarios, covered later in the post.

It may come up as a conceptual question regarding cross validation or bias-variance tradeoff, or it may take the form of a take home assignment with a dataset attached.

Lastly, this post is part of an ongoing initiative to ‘open-source’ my experience applying and interviewing at data science positions, so if you enjoyed this content then be sure to follow me for more stuff like this.

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