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Data science is a complex discipline that identifies significant information drawn from gigantic amounts of structured and unstructured data. Probably the hardest part of this field of knowledge is to learn how to make sense of all this data, and transform this immense amount of scattered info into meaningful, actionable insights. A competent data analyst knows how to spot those patterns that enable organizations to devise effective strategies, find new opportunities, and enhance their marketing efforts.
A job in data science is one of the most well-paid ones available, and data scientists are always sought after by even the largest company. Is it really possible to teach yourself data science? Can you go from just basic IT skills to becoming a master analyst? The answer is yes, provided you choose the right courses and take them with due diligence. Here we will present you with a roundup of the most important data science concepts you must learn to become a self-taught data scientist, all of which you can learn from the comfort of your own home. You can take all these courses through Coursera for less than $100 each. (To learn more about what a data scientist does, see Job Role: Data Scientist.)
Plain and simple, first things first. You cannot become a data scientist unless you understand what data science really is, and an introductory course that provides you an overview of this discipline is the first step you should take. Core concepts include why and how data science is so important for business and how it can be applied. You must be able to understand what regression analysis is, and how the process of mining a data set works, as well as what tools and algorithms you are going to use on a daily basis to master this discipline.
The best courses are those which also focus on methodology, so you can be sure that the data that you will collect is used for hands-on problem-solving in a relevant way. The basics should include understanding how to properly manipulate it in order to tackle the most common issues, and how to make sense of the feedback after a model is built and deployed.
An introductory course that teaches you statistics by application is the best place to start learning data science, and Python programming represents the most basic skill required to understand this field. Before working with data, you need to understand how to extract it in its rawest form, and Python represents the most basic instrument for manipulating and refining it.
The first courses you need to take should teach you the fundamentals of the Python programming environment needed to make sense of CSV files and to find your way through complex data structures. Core concepts include understanding t-tests, sampling and distributions, how to query a Pandas DataFrame structure, and how to extract, clean and process tabular data.
The vast majority of data is mined from databases, and at least a portion of it exists in a structured form. SQL stands for “Structured Query Language” and it’s the most powerful language to “speak” with databases in order to understand them, explore every nook and cranny, and extract all the meaningful data you need for the problem at hand. Knowing how to work with SQL, create database instances in the cloud, run SQL queries, and access databases and real-world data sets from Jupyter notebooks is a must-have skill set for any data scientist.
Some degree of knowledge in statistics is a necessity in data science. Although statistics is a really broad field, a data analyst requires a grasp of at least some concepts in statistics and probability theory to provide practical insights to businesses and organizations. (For more on data science, see 12 Key Tips for Learning Data Science.)
You need to combine theory with practice by learning core concepts such as distribution, hypothesis testing and regression, as well as the fundamental Bayesian probability theory. Most machine learning modules are, in fact, built on Bayesian probability models. The Bayesian approach is an intuitive one that moves from probability to the analysis of data and allows for better accounting of uncertainty as well as providing actionable statements of assumptions that can be used in practice.
To master data science you need to learn how to solve various computational problems with algorithmic techniques. Algorithms are used to manipulate data through efficient data structures. You need to learn how to implement these structures in different programming languages, what to expect from them, and how to break large problems into more granular pieces. There are many strategies that must be learned to design an efficient algorithm, such as how to keep a binary tree balanced, how to resize a dynamic array, and how to solve problems recursively.
Machine learning is the science that allows computers to act outside the boundaries of the scripts they’re programmed to run. It’s a pervasive science that has a lot of applications in the real world, and data mining is one of them. But to approach machine learning you need to possess all the skills mentioned above. Machine learning algorithms need to be programmed with Python, and statistical approaches are the most effective ones to “teach” a machine how to become smarter.
The whole field of machine learning is extremely vast, and includes various subtopics such as supervised and unsupervised learning, model evaluation and deep learning. Although you do not necessarily need to dive as deep as learning how to program the most advanced neural networks, the more you know about the many applications of machine learning in data science, the better.
It doesn’t matter whether you’re a university student looking for new ways to broaden your horizons, or a professional wanting to enhance his or her resume. Learning these key data science concepts is all you need to give yourself a competitive advantage in the industry.