12 Steps For Beginner To Pro In Data Science In 12 Months!
By: Bharath K
Tags: Machine Learning, Success, Artificial Intelligence, Productivity, Data Science

The 12 Ultimate Steps to Follow over the course of the year —

Beginners:

1. Learning Programming

Programming is the heart of Data Science. It is essential to have some knowledge of programming to succeed as a data scientist.

Coding and programming knowledge is almost a compulsory requirement to have an in-depth understanding of the field of data science. Even if you lack the coding or programming skills, but you have to interest to learn and pursue these techniques, then this should not be a major concern.

In my opinion, the two main languages you should know to do well in the field of Data Science in Python and SQL. The reason I have suggested these two languages is because Python is a fantastic starting choice for most beginner-level programmers, even for people with no coding experience. Hence, it is probably the best starting point because of its simplicity.

Structured Query Language (SQL) is a domain-specific language used in programming and designed for managing data held in a relational database management system (RDBMS), or for stream processing in a relational data stream management system (RDSMS). It is particularly useful in handling structured data, i.e. data incorporating relations among entities and variables.

SQL will play a great role in analyzing the various data that you will gather while working in Data Science. It will help you to create systematic arrangements to solve complex data related queries. It also helps you to maintain and manage the huge amount of data that you will eventually be dealing with.

I have spoken about Python in most of my articles and have immensely praised its versatility and simplicity in the field of data science. I will make sure I write an article covering the complete beginners guide to Python next year to help you all in understanding some basic concepts better. You can check out the bonus section for more information related to Python.

Bonus:

This Section will involve parts that I have discussed previously in my other articles. However, it is extremely beneficial for beginners, and would recommend giving it a read for the new viewers. If you already read this, feel free to skip ahead to the next step.

Python is an object-oriented, high-level programming language that was released way back in 1991. Python is highly interpretable and efficient. Python is versatile, and thanks to its resourcefulness, it is a suitable fit for Data Science. I initially started with languages like C, C++, and Java. When I finally encountered Python, I found it to be quite elegant, simple to learn, and easy to use.

Python is the best way for anyone, even people with no prior experience with programming or coding languages, to get started with machine learning. Despite having some flaws, like being considered a “slow” language, Python is still one of the best languages for AI and machine learning. Although there are a variety of other languages such as Julia, Golang, etc., which might be quite competitive against Python in the future years, the latter remains the better choice at this point.

The main reasons for the popularity of Python for Data Science despite other languages like R is as follows —

As mentioned previously, Python is a simple language and is overall consistent. The rapid increase in popularity in comparison to other programming languages makes it a suitable pick for beginner-level programmers. Has extensive resources concerning a wide range of libraries and frameworks for supporting Data Science. Versatility and platform independence, which means Python can import essential modules built in other programming languages as well. It has a great community with continuous updates. The Python community, in general, is filled with amazing people, with constant updates made to improve Python.

To get started with Python, you can download it from here.

Understanding the basics of the Python programming language is undoubtedly the most important aspect to master Python. There are many key concepts like keywords and identifiers, variables, iterative statements like “for” loop, “while” loop, the comment lines, control statements, and so much more. We will cover all this extensively in a future article!

2. Understand Math

If programming is the heart of Data Science, then Math is the brain!

Mathematics, I find, is one of those subjects you either learn to love or end up loving to hate. Some find math as an amazing subject while others find all these number’s thing kind of boring. It does not matter which side of the spectrum you are on because math is, fortunately, or unfortunately, one of the most fundamental requirements for machine learning and data science.

Mathematics is an essential requirement for data science. Linear algebra, calculus, probability, and statistics are the most significant concepts that you need to know in order to conquer all the mathematical aspects of data science.

A high school understanding of the basics of these concepts would suffice for a beginner to enter into the universe of data science. However, if you are not too confident with these concepts or need a brief brushing, then I would highly recommend checking out reading some articles on TDS because they explain most concepts with simplicity and ease. YouTube videos are also a great alternative option to learn these concepts.

Mathematics is required for building predictive machine learning models, understanding probabilistic and deterministic approaches to solving Bayesian and other similar problems, understanding backpropagation in deep neural networks, analyzing gradient descent, and so much more.

If you are interested in learning mathematics from scratch, below are some references that you might useful.

Useful Resources To Consider:

1. Khan Academy: Best place for you to start off with learning basic mathematical concepts like linear algebra, calculus, and probability and statistics.

2. 3Blue1Brown: One of the best YouTube channels to check out some brilliant in-depth explanations and intuitively understand concepts much better.

3. Books: If you are more into reading books, then do a quick Google search to check out some outstanding resources for the best books to learn Mathematics. You should prefer separate books for each specific topic for better practice.

3. Intuitive Understanding Of Basic Concepts

Develop an intuition of the concepts. Don’t mug up!

The subject of Data Science is a humongous field, and there are tons of theoretical aspects to be understood by beginners of the field. It is completely alright to take your time studying the concepts by developing a strong intuitive as well as a practical understanding.

Since you are now in the third step, you will probably have a decent understanding of the Python programming language and the math required for machine learning. Try to implement various math concepts such as Euclidian distance and similar concepts in Python from scratch without utilizing any machine learning libraries.

Even if it is just a simple Machine learning algorithm, I would highly recommend not to use the scikit-learn library or similar helpful tools for easier implementations. Instead, try to detect the best possibilities and execute them from scratch on your own. This helps to improve skills and gain an overall better understanding of these concepts.

I would highly encourage the viewers to check out one of my previous articles, where I have implemented the matrix multiplication from scratch without using any machine learning algorithms.

You can do this from the link provided below. Performing such tasks will help you to gauge a better understanding of the various concepts and appreciate the quality of the libraries that we will study in the next step.

4. Dwelling Into The Various Libraries

The best part about learning the Python programming language is the assistance of a variety of useful and fabulous libraries that this language offers us. The installations of most of these libraries are simple and efficient. They are productive and enables the Data Science enthusiasts to complete complex tasks within a few lines of code. (This can be noticed from the article that I had previously suggested.)

Python offers a wide range of library modules and frameworks, making it one of the most compatible languages for constructing machine learning models and working on data science projects. The most commonly used python modules include numpy for numerical operations, scipy for scientific operations for dealing with something like sparse matrices, pandas to view the datasets in an organized manner, matplotlib and seaborn to visualize the data, analysis, and graphs of models constructed.

You can also utilize the scikit-learn module in python for the development of machine learning models through the various algorithms options available in the sklearn library. Also, you construct deep learning models by making use of frameworks such as TensorFlow and Pytorch. The next mini-section involves a bonus section that will cover five of the more popular and useful data science libraries that a beginner must know about.

Bonus:

This Section will involve parts that I have discussed previously in my other articles. However, it is extremely beneficial for beginners, and would recommend giving it a read for the new viewers. If you already read this, feel free to skip ahead to the next step.

In this part, we will discuss five of these significant library modules that are used in python for solving Data Science tasks. There are tons of frameworks available in python, but these five mentioned in this section of the article will cover the basic requirements to get started.

1. Pandas —

The Pandas module is an open-source library in python to create data frames, which is extremely useful for organizing the data. Pandas is used extensively in the field of data science, machine learning, and deep learning for the structured arrangement of the data.

The data frame created in pandas is a 2-dimensional representation of the data. After importing the Pandas library as pd, you can visualize the tabular data of your liking. An example of this is as shown below:

Overall, the Pandas module is a fantastic library for systematic viewing of the data, and it also allows a wide variety of operations that can be performed.

2. Matplotlib —

The Matplotlib module is one of the best tools for the visualization of the data frames or any other form of data. Matplotlib is used to visualize the data for exploratory data analysis in data science. It is extremely useful to understand the kind of data we are dealing with and to determine what is the next action that must be performed.

The library offers an extensive variety of visualization functions such as scatter plot, bar plot, histograms, pie chart, and many other similar functions. Import matplotlib.pyplot module as plt for performing visualization tasks using matplotlib. An example of these can be seen below —

Scatter Plot

Bar Graph

The scatter plots and bar graph plotted using matplotlib is shown in the figures. An advantage of the module is that it is very simple to use and efficient at providing visualizations. It can also be combined with the seaborn library for a more visual and aesthetic appeal.

3. NumPy —

The NumPy library stands for Numerical Python. The numpy library is one of the best options for performing computations on matrix operations. It supports multi-dimensional arrays. An extensive amount of mathematical and logical operations can be performed on arrays. By converting lists into numpy arrays, we can perform computations like addition, subtraction, dot product, among many others.

The use cases of numpy are applicable in both computer vision and natural language processing projects. In computer vision, you can use numpy arrays for visualizing the RGB or grayscale images in a numpy array and converting them accordingly. In natural language processing projects, you usually prefer to convert the text data into the form of vectors and numbers for optimized computation. Import numpy as np, and you can convert the text data into categorical data, as shown below:

4. Scikit-learn —

The scikit-learn module is one of the best tools for machine learning and predictive data analysis. It offers a wide range of pre-built algorithms such as logistic regression, support vector machines (SVM’s), classification algorithms like K-means clustering, and a ton more operations. This is the best way for beginners to get started with machine learning algorithms because of the simple and efficient tools that this module grants access to.

It is open-source and commercially usable while granting accessibility to almost anyone. It is reusable and supported by libraries such as NumPy, SciPy, and Matplotlib. import the sklearn module to run the scikit-learn code. Below is a code example for splitting the dataset we have into the form of train and test or validation data. This is useful for training and evaluation of the models.

5. NLTK —

The NLTK library stands for the natural language toolkit platform, which is one of the best libraries for machine learning of natural language processing data. Natural Language Processing (NLP) is a branch of AI that helps computers to understand, interpret, and manipulate human language.

The NLTK library is very well suited for linguistic-based tasks. It offers a wide range of options for tasks such as classification, tokenization, stemming, tagging, parsing, and semantic reasoning. It allows the user to chunk the data into entities that can be grouped together to produce a more organized meaning. The library can be imported as nltk, and below is an example code for the tokenization of a sentence.