Easy Steps to learn Data Mining and Data Science
Easy Steps to learn Data Mining and Data Science
Data mining and data science have become a necessity in the modern world. In business, data mining helps in the discovery of relationships and patterns in data. Data science helps in the extraction of this data—this aids in better business decisions. You should know that data mining can help spot sales trending and predict customer loyalty.
Today, one of the best careers is becoming a data scientist. The reason is that over the past two decades, the world has experienced a massive explosion of data. It is thanks to cloud computing, social media, search engines, and more.
Research shows that the demand for data scientists increased by more than 50% as of 2018. Thanks to the rise of “big data,” the need for data scientists to make these data conform to some sought of a rule is on the rise.
Want to become a data scientist? Are you searching for resources to learn data mining?
In this post, we’ll discuss the steps to learn data mining and data science.
1. Learn Programming Languages
Due to the growth of the Internet of Things and Big Data, data mining and data scientists are in high demand. Companies are looking for skilled data scientists able to extract data for valuable insights. Using this data, a business can remain competitive and stay ahead of the curve.
To become a skilled data scientist, you must learn programming languages. Data mining relies heavily on programming languages such as:
- Python
- R
- C++
- Java
- Matlab
- SQL
- SAS
Learning all the programming languages can be overwhelming. What I recommend is starting with Python. Python is versatile and has several libraries giving you the freedom to develop your apps. It’s easy to use, open-source with a vibrant community, and great for prototypes.
To get started with Python, I recommend “Automate the Boring Stuff with Python.” This book is excellent for beginners and helps to explain Python programming from scratch. I also recommend the following resources:
Learning R, C++, Java, MatLab, SQL, and SAS is essential too. Here are resources to help you get started.
2. Learn Linear Algebra and Statistics
Besides learning Python, skilled data scientists must learn linear algebra, math, and statistics. Fundamental knowledge in statistics, math and linear algebra gives you the skills to analyze results from data processing algorithms.
Much of this can be learned in college and university, but if you did not get the chance, I recommend The Elements of Statistical Learning by Hastie, T, et.al. The book will help you learn Linear methods of classification and linear ways of regression. It will also help you learn basis expansions, and regularization, among others.
If you have a little understanding of machine learning, you need to learn a little math. This knowledge will help you understand how algorithms work and their limitations. I recommend the following book - A comprehensive beginners guide to Linear Algebra for Data Scientists.
This book covers different topics such as the representation of problems in Linear Algebra, and eigenvectors.
Other books I recommend, especially if you want to have a better understanding of neural networks and the mathematical principles behind them are:
- Neural Networks and Deep Learning
- Deep Learning
- A Complete Guide To Maths And Statistics For Data Science
3. Learn to Use Data Science and Visualization Software
While learning Python and R is crucial to data scientists and data mining. Learning to use available data visualization tools is important too. Data visualization software helps to analyze big data sets and present them in the form of visuals.
They include images, graphs, and more. With data visualization tools, you will not find it challenging to understand the insights collected.
One of the best data visualization software for data scientists is Tableau. This is an interactive tool used for effective data visualization and analysis. It features a drag and drop interface allowing you to perform different functions, fast.
With this software, you don’t have to write any custom code.
Another tool we recommend is QlikView. Similar to Tableau, this business intelligence, too, can turn big data sets into useful information. What you need to know is that the tool integrates different data sources such as Impala, EC2, and HP Vertica.
Lastly, we have Datawrapper, a data visualization tool for non-technical users. It has a user-friendly interface allowing data scientists to create visualizations without coding.
Best for beginners who want to start a career as data scientists, it allows users to export charts and even select multiple map types.
Other data visualization tools we recommend include:
- D3
- Microsoft Power BI
- Plotly
- HighCharts
- FusionCharts
4. Join Webinars and Online Courses
You can now learn data mining and data science from the comfort of your home. In fact, you can do so without spending a fortune. Experts project that the demand for ‘armchair data scientists” will outstrip that of traditionally qualified data scientists.
What you need to know is that the resources for learning are free, but you may have to pay to receive the certification. Here are the top three online courses for data mining and data science.
Learn Data Science by Dataquest is a paid-for course with proprietary content. But it offers several free introductory modules for you to get started. The course covers essential topics such as visualizing data, data mining, constructing algorithms in R, and Python. It also includes working with data.
For a full, ad-free experience and certification, select the monthly subscription.
Data Science by Havard is a free online course. Since the materials are available for free, you can study at your own pace. This course is part of a data science degree where you get to learn maths, statistics, and programming.
For a crash course, we recommend the Data Science Crash Course via Coursera. It touches on what data science is, how it works, and its applications. This is a relatively short course, and you can complete it in under a week. If you want to learn the data mining terminology, start with this course.
Other resources we recommend include:
When joining these online courses and webinars, commit to learning. Do not get in the habit of signing in to watch only a few videos. Watch every single video.
5. Practice
Studying theory alone is not enough. You need to practice experience. The first option is big data sets to analyze, such as free public datasets. Several online platforms hold big data sets.
They include:
For more data sources, click here.
Besides using free public datasets, you can try your hand at Kaggle. Kaggle is an online platform that hosts over 19,000 open data sets and 200,000 public notebooks. This platform allows you to grow your data science skills by competing in existing competitions.
To get started, sign in using your Gmail and click “Compete” on the navbar. Kaggle recommends beginning with the Titanic Competition. This competition allows you to predict survival on the Titanic. This competition also helps you familiarize yourself with ML basics.
Wrapping Up
There you have it—everything you need to get started in data mining and data science. From the list above, you can see that most of the resources are available online. While some require paid subscriptions even when it comes to certifications, most are free.
As such, you can learn from the comfort of your home and on a budget. To extend your knowledge even further, you can read textbooks, join peer groups and social networks. One of the best groups is ACM SIGKDD that organizes the KDD conference.
