Source: Deep Learning on Medium
Data science is defined as a set of tools and techniques using data to obtain insights and information that provide some level of value. Data science is evolving fast and has a wide range of possibilities surrounding it and so to limit it by that basic definition is kind of elementary. An extension of that definition would be that data science is a complex combination of skills such as programming, data visualization, command line tools, databases, statistics, machine learning and more, in order to analyze data and obtain insights, information, and value from vast amounts of data.
Data science has gone from being a newly-coined term to being one of most sought-after disciplines in the professional world. Data scientists are professionals which collect -often messy- data and combine statistics, computer science and data analysis to provide insight to help form calculated and well-informed strategies for organisations, ranging from large enterprises to start-ups. It’s a trendy role, named the best job in USA for three years running, paying a median base salary of $105,000, and an average base salary of £45,000 in the UK.
Despite the popularity of the job, businesses across various industries are facing a big problem: there are not enough qualified data scientists out there. In the UK alone, there are more than 4000 active Data Scientist vacancies just advertised on Glassdoor, signifying vast demand for the profession. IBM correspondingly predicts the demand for Data Scientists will soar 28% by 2020 and goes on to state that data science skills are one of the most challenging skills to recruit, potentially disrupting ongoing product development and strategic planning if positions are not filled.
It is widely assumed that you will need a Ph.D. or M.Sc. to pursue a career as a Data Scientist, though research into job advertisements show that this is simply not the case, with a generous majority not requiring computer science graduate degrees. As much as 78% of advertisements require that the applicant have relevant work experience to showcase the skills required to be successful in the role.
In this article we will take a look at some of the steps you can take to begin your journey into data science without needing a degree but with an in-depth knowledge of a set of skills.
1. Python Programming
If your aim is growing into a successful coder, you need to know a lot of things. But, for Machine Learning & Data Science, it is pretty enough to master at least one coding language and use it confidently. So, calm down, you don’t have to be a programming genius.
For successful Machine Learning journey, it’s necessary to choose the appropriate coding language right from the beginning, as your choice will determine your future. On this step, you must think strategically and arrange correctly the priorities and don’t spend time on unnecessary things.
Python is the perfect choice to make your focus on in order to jump into the field of machine learning and data science. Python is ranked at number 1 for the most popular programming language used to implement Machine Learning and Data Science. It is supported by most of AI development frameworks. It is a minimalist and intuitive language with a full-featured library line which significantly reduces the time required to get your first results.
2. Learn the Basics of Statistics & Probability
Probability and Statistics form the basis of Data Science. The probability theory is very much helpful for making the prediction. Estimates and predictions form an important part of Data science. As a data scientist, you’ll be called upon to use statistical methods to analyze and interpret data. You must know statistics to infer insights from smaller data sets onto larger populations. This is the fundamental law of data science.
3. Learn the Basics of Linear Algebra
Linear Algebra is central to almost all areas of mathematics like geometry and functional analysis. Its concepts are a crucial prerequisite for understanding the theory behind Data Science. You don’t need to understand Linear Algebra before getting started in Data Science, but at some point, you may want to gain a better understanding of how the different algorithms really work under the hood. So if you really want to be a professional in this field, you will have to master the parts of Linear Algebra that are important for Data Science.
4. Artificial Intelligence, Machine Learning & Deep Learning
You can think of deep learning, machine learning and artificial intelligence as a set of Russian dolls nested within each other, beginning with the smallest and working out. Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is AI, but not all AI is machine learning, and so forth.
Without further delay, here is a list of online courses presenting the best introduction to the world of data science and predictive models building:
5. Machine Learning Libraries
Creating Machine Learning models that can accurately predict the outcome or solve a certain problem is the most important part of any Data Science project. The single most important reason for the popularity of Python in the field of AI and Machine Learning is the fact that Python provides 1000’s of inbuilt libraries that have in-built functions and methods to easily carry out data analysis, processing, wrangling, visualizing, modelling and so on. Here’s a list of the top Python libraries for Machine Learning:
Scikit-learn: One of the most useful Python libraries, Scikit-learn is the best library for data modelling and model evaluation. It comes with tons and tons of functions for the sole purpose of creating a model.
XGBoost: XGBoost which stands for Extreme Gradient Boosting is one of the best Python packages for performing Boosting Machine Learning for structured data. Libraries such as LightGBM and CatBoost are also equally equipped with well-defined functions and methods.
NumPy: Functions provided by NumPy can be used for indexing, sorting, reshaping and conveying images and sound waves as an array of real numbers in multi-dimension.
SciPy: Built on top of NumPy, the SciPy library is often used to compute mathematical equations that cannot be done using NumPy.
Pandas: Pandas is another important statistical library mainly used in a wide range of fields including statistics, finance, economics, data analysis and so on. Pandas relies on NumPy arrays for the purpose of processing Dataframes.
Matplotlib: Matplotlib is the most basic data visualization package in Python. It provides support for a wide variety of graphs such as histograms, bar charts, power spectra, error charts that are essential for Exploratory Data Analysis.
Seaborn: In comparison to Matplotlib, Seaborn can be used to create more appealing and descriptive statistical graphs.
6. Deep Learning Frameworks
The biggest advancements in Machine Learning and Artificial Intelligence have been through Deep Learning. With the introduction to Deep Learning, it is now possible to build complex models and process humongous data sets. Thankfully, Python provides the best Deep Learning packages that help in building effective Neural Networks.
In this blog, we’ll be focusing on the top Deep Learning packages that provide built-in functions to implement convoluted Neural Networks. Here’s a list of the top Python libraries for Deep Learning:
Keras is considered as one of the best Deep Learning libraries in Python. It provides full support for building, analyzing, evaluating and improving Neural Networks. Keras is built on top of Theano and TensorFlow Python libraries which provides additional features to build complex and large-scale Deep Learning models.
One of the best Python libraries for Deep Learning, TensorFlow is an open-source library for data-flow programming across a range of tasks. It is a symbolic math library that is used for building strong and precise neural networks. It provides an intuitive multi-platform programming interface which is highly-scalable over a vast domain of fields.
Pytorch is an open-source, Python-based scientific computing package that is used to implement Deep Learning techniques and Neural Networks on large datasets. This library is actively used by Facebook to develop neural networks that help in various tasks such as face recognition and auto-tagging.
7. IDEs for Machine Learning Development
IDE’s are very important in order to execute your own code. An IDE typically contains a code editor, a compiler or interpreter, and a debugger accessed through a single graphical user interface (GUI). Different users have different choices when it comes to the type of python IDE they want to use. Some of the popular IDE’s to develop machine learning models are Jupyter Notebook, PyCharm, Spyder and VS Code.
Most of the time Jupyter Notebook is by far the favorite option for data practitioners when it comes to building proof of concepts. It is an incredibly powerful tool for interactively developing and presenting data science projects. A notebook integrates code and its output into a single document that combines visualizations, narrative text, mathematical equations, and other rich media. When you want to run a chunk of code altogether, PyCharm is the better choice and if you want your whole project to look organized, has a lot of files and you want to make it look in a structured way.
8. Deploy Machine Learning Models using Flask
You’ve built a machine learning model using python. It turns out to be super cool with good prediction results. It would be nice if there’s a way you can share this thing with your friends and family. So, how can we do this? Is there a simpler way of doing this? The easiest way of doing it is by deploying the model using flask.
Thanks for reading my article and I hope you gain something from it. I am working on creating some tutorials, guides, and a complete course on data science to help all those who need it and I plan to release it very soon