Did you hear your friends talk about taking a data science tutorial?
Did it make you wonder what is data science and why have data science tutorial for beginners garnered so much attention?
Why are 97,000 jobs for data scientists vacant in India? All of this will be laid to rest here.
Data science has become the talk of the town. Every industry and every business talks about data science and how to use it to their advantage.
Data science is also one of the most welcoming fields for freshers. The field is relatively new. The lack of experienced personnel, coupled with the demand for data scientists has opened up the industry for freshers.
This post will talk about data science in detail. By the end of it, you will know why a data science tutorial is the best thing you can do for your career right now.
What Is Data Science?
Over the last few decades, the amount of data that humans have generated has risen exponentially, and its growth continues.
The internet and technology have played an enormous role in this. Now, companies have an unprecedented amount of data on their hands and need a way to put this data to work.
Data science is the way to do this. As any data science tutorial point would tell you, it is a multidisciplinary field.
It is a union of algorithms, inference, statistics, and technology that converts structured, as well as unstructured data, into valuable products and information.
Terms like artificial intelligence, machine learning, big data, and deep learning are often used interchangeably with data science in the general vocabulary. However, these are different areas that contribute to data science.
(i) Artificial intelligence focuses on creating machines that can think and behave as humans do.
(ii) Machine learning creates tools that extract useful information from data.
(iii) Big data deals with systems and tools that can handle tremendous amounts of data.
(iv) Deep learning focuses on creating multi-layered neural networks to work on more advanced algorithms than machine learning.
What is data science‘s role in all of this? It combines all of these methods to add value to data, create visualizations, gather information, and make business decisions based on it.
Data science tutorial for beginners should cover the two facets of data science – getting information and creating data-based products. You can mine the data and look at it from a granular level and uncover customer behavior, trends, etc.
The findings are then shared with the higher brass who make decisions based on them. You can also create products that analyze the data by running algorithms on it and produces results in real-time.
The product integrates into the applications. There is no intermediate decision-maker involved.
When you start your data science tutorial, it may seem like the subject is similar to data analysis or business intelligence. There are areas where they overlap, but they are not the same.
(i) Data analysts also process data to gain business information. Data scientists do much more than that. They use machine learning algorithms to look at raw data. Along with information, they create products.
(ii) A data analyst mostly works with structured and processed data, whereas data processing is a part of the data scientist’s job description.
(iii) Data science focuses on the past, present, and future, whereas analysis only deals with the past and the present.
If you want an idea of what to expect for a data science tutorial, then this video should tell you what you need to know.
Why Do We Need Data Science?
As mentioned earlier, the amount of data generated every day has reached tremendous proportions. Simple tools are no longer enough to keep up with this production.
You need specialized algorithms that can handle immense quantities of data at a considerable speed.
Moreover, these algorithms need to work on raw, unstructured data generated from a variety of sources. All of this calls for complex, efficient, and robust analytical tools and algorithms.
It is the job of data scientists to develop these tools and fine-tune them to meet the specific requirements of the problem at hand.
Data science is a versatile field. The same concepts that are used to create product recommendations can also be used to detect financial fraud.
Just think about that for a second. Do you realize that every business, big or small, uses computers for their day to day operations? Data science will become just that in a few years.
A data science tutorial point will enlighten you about how data science is being used in different industries. Here are a few examples:
(i) The past behavior of customers is used to understand their likes and dislikes and recommend products.
(ii) The data from satellites, ships, airplanes, radars, etc is used to predict the weather.
(iii) Brands can devise marketing strategies that target the right set of people and increase their revenue.
(iv) Self-driving cars make use of the present traffic conditions and data from sensors within the car to drive safely to a destination.
Your data science tutorial point will also involve working on a project in one of these areas. Hands-On training is just what you need to become familiar with the challenges in implementing a data science project.
If you have a specific industry that you want to become a part of, then the more industry-specific projects you do during your data science tutorial, the better it will be for your career.
What Are the Prerequisites for a Data Science Tutorial for Beginners?
You cannot start your data science tutorial without having some basic knowledge of the underlying subjects. As mentioned above, data science is a multidisciplinary field.
You cannot expect the data science tutorial for beginners to cover all the concepts used in the process.
You would have come across these prerequisites during your college coursework. If you have forgotten the basics, then it is better to familiarise yourself on them again before starting the data science tutorial.
(i) Linear Algebra – You will be working with data that will be in the form of matrices. You should know how to manipulate matrices.
(ii) Statistics – Calculating mean, standard deviation, and testing hypothesis will be a regular part of your job.
(iii) Probability – You will be predicting future events. Probability concepts are key to understanding the likelihood of the occurrence of an event.
(iv) Calculus – Many algorithms will use integrations, differentiation, and optimization techniques.
(v) Machine learning – Machine learning and data science overlap quite a lot. The concepts that you need for data science will be covered in your data science tutorial. However, it will be easier for you to understand them if you have a background in machine learning.
(vi) Programming – What is data science without the ability to program? Python and R are the most common languages used by data scientists. Python is the preferred language as the libraries such as SciPy, NumPy, etc make it easier to implement complex algorithms.
What Are the Steps Involved in a Data Science Application?
Data science is not a one-step process. It is a systematic process that involves many stages. A mistake at any stage can impact the final result.
Every step is crucial, and every step demands a different set of skills. This is why data scientists should be experts in a host of concepts.
The data science tutorial should cover these steps in-depth. Here is an outline of the lifecycle of data science application.
Data Discovery
Everything you do requires data and collecting data is the first phase. You need to ask the right questions and get your hands on as much relevant information as possible.
Proper understanding of the problem and the industry will help you narrow down the data required for the problem.
Data Preparation
The data you collected may have missing values and outliers. It may also be unstructured. You need to clean it and get it to a format that can be analyzed by the model.
Modelling
At this phase, you identify the models to be used for the problem at hand. Depending on the situation, you may need to use more than one model.
This phase uses machine learning algorithms. Most of your data science tutorial point will concentrate on this aspect.
Operationalize
The model you developed is ready to be deployed in the real environment. However, this step can throw up certain unexpected issues. You will have to fine-tune your model to avoid them.
Communicate
You need to communicate the results you obtained to the customer or the management. At this stage, you will have to translate your results into layman terms that everyone can understand.
Must-Know Data Science Tutorial Algorithms
Every data science tutorial will have at least a few sessions dedicated to teaching you the algorithms that you would use to build your models.
Each of these algorithms is suited for a particular situation. There are so many algorithms that it would be unfair to expect you to learn all of them.
However, you should be aware of most of the algorithms and their applications. You need to know a few algorithms thoroughly.
Figuring out which ones you should spend most of the data science tutorial for beginners on is a simple task.
You may have a few areas of interest or industries that excite you. Look at the data science applications in these industries.
Consider the problems they face and which algorithms are best suited for solving them. Viola! You have narrowed down your choices.
If you do not have any specific industry in mind, then consider the most common applications of data science and the algorithms they use.
Let us have a look at some of the most popular algorithms used by data scientists that you should learn in your data science tutorial.
Principal Component Analysis
Principal Component Analysis is used to reduce the number of variables in your data without losing any information. You combine those variables that have a high correlation to form a new set of variables that are known as principal components.
K-Means Clustering
K-means clustering identifies the underlying clusters within the dataset. The iterative algorithm forms the clusters organically.
All you do is specify the k or the number of clusters. It is very useful in grouping customers by their behavior.
Logistic Regression
Logistic regression is used to predict the outcome based on the past values of the variables. The output variable may be dependent on one or more independent variables. The relationship between these variables governs the outcome.
Neural Networks
Neural networks mimic the operation of the human brain cells and help you classify data. Once you train the network on a set of labeled data, it can then successfully classify any dataset that it is fed.
There are different types of neural networks such as feed-forward, recurrent, etc.
Conditional Random Fields
CRFs are a sequence-based modelling technique where every sample is classified by considering the neighboring samples as well. You use the known relationship between the samples to classify the sequential data.
How to Become a Successful Data Scientist
The data scientist job has been described as the sexiest job in the market, and rightfully so. It encompasses many different areas.
You are not limited to just one. You are a statistician, programmer, developer, and a mathematician. Which other jobs can offer all these perks?
If you are interested in becoming a part of this exciting field, then you should take the data science tutorial master course offered by Digital Vidya.
The master course also includes foundation courses on Python and statistics. The course also includes data science using Python and R.
Both Python and R are common languages used for data science. Being trained in both will add spark to your resume. It will bring more employers knocking on your door.
There are various resources available online to help you whenever you need guidance. One of the advantages of studying data science is that there is an enthusiastic online community. You can always get your doubts cleared by the community.
If you want to test out your knowledge of data science and gain some practical experience, then you should consider taking part in competitions such as Kaggle.
If the competition seems too daunting, then you can try solving the competition problems by yourself.
Becoming a data scientist takes determination and hard work. But the payoffs make it worth the effort. Join the Data Science Master Course today to become a part of the growing data science workforce.
