data science tutorial
data science tutorial
Data Science
Data Science is the most challenging field of the 21st generation. Every type of IT industry is looking for candidates with knowledge of data science. We are providing the basic and some advanced concepts related to the operation that can be performed upon the data with the help of different technologies.
In this tutorial, we will discuss the following topics:
What is Data Science
The need for Data Science
Jobs in Data Science
Types of Jobs in Data Science
Prerequisites of Data Science
Components of Data Science
Tools of Data Science
Work Flow of Data Science
Life Cycle of Data Science
BI (Business Intelligence) Vs. Data Science
Applications of Data Science
Data Science Vs. Big Data
Conclusion
What is Data Science?
Data Science is the multi corrective field that uses scientific methods, processes, algorithms, and systems to extract the knowledge and insights from the ordered and shapeless data. It is the future of artificial intelligence.
Data Science is a deep study of a massive amount of data that is involved in extracting meaningful observations from raw, structured, and unstructured data, which is processed by using the scientific methods, different technologies, and algorithms. It is the same concept as per the data mining and big data.
Data Science is the “concept to unify the statistics, data analysis, machine learning, and their related methods” in order to understand and analyze the original phenomena related to data. It is crucial for better marketing. Most companies use the data to analyze their marketing strategies and make a better advertisement.
The main purpose of data science is to find the patterns within the data and uses several techniques to analyze and draw the perceptions from data. In Data Science, the data scientist has the responsibility of making the predictions from the data. So, a data scientist aims to derive the conclusions from the whole data. With the help of these conclusions, the data scientist can support the industries in making smarter business decisions.
What is Data Science
Figure: Use cases of Data Science.
Example- Getting the ride from Uber is easy, how
Simply, the user can open the application and set his pickup point and drop location, and then the cab is booked. If anyone has booked the taxi through Uber or Ola, the user can get the expected price and time to cover the specific distance.
How are these apps capable of showing all the information? The answer to this is the concept of Data Science. The predictive analysis in Data Science is helping Uber to show the user, pickup point, drop location, and the arriving time.
Getting the ride from Uber is easy, how
Data Science uses powerful hardware, programming systems, and efficient algorithms to solve data-related problems.
In short, we can define the Data Science is all about:
Ask correct questions and analyzing the raw data
Visualize data for getting a better perspective.
We are modeling the data by using various complex and efficient algorithms.
Understand the data to make a better decision and finding the final result.
we can define the Data Science
The need for Data science
A few years ago, we didn’t have a large amount of data, and data was available in the structured form, which could be stored in the excel sheets and processed by using simple Business Intelligence tools.
But, now the data has become vast. So, we have a lot of data to be processed, approximately 2.5 quintals bytes of data generating per day. There are some points related to the need for Data Science that is given below:
IT Industries needs data to help them and make careful decisions. Data Science is a piece of raw data into meaningful observations.
All the Industries require Data Science for handling the large volume of data, and this thing increases its importance.
Data Science is used by almost every type of industry, but some major sectors are healthcare, finance, Banks, business, startups, etc.
Data Science is a career for the future. Every industry is becoming data-driven, and innovations are being made every day.
The industry requires a data scientist to support them in making smarter decisions. Everyone needs data scientists to predict the information.
Data Science is very important for better marketing. The industries are using data to analyze their marketing strategies and generate improved advertisements. The Decisions can be made by analyzing the customer’s feedback; therefore, industries are using data science to run a particular campaign.
Data Science is also working for automated transportation, such as creating a self-driving car, which is the becoming future of transportation.
Every company needs data to work, grow, and for improvement of their business. In Data Science, Handling such a vast amount of data is a challenging task for every organization. We need some complex, powerful, and efficient algorithms to handle, process, and analyze the data.
Jobs in Data Science
According to different surveys, the Data Scientist profile is the most trending job these days due to the increasing demand for Data Science. So, it is also known as the “hottest job title “nowadays.
The Data Scientists are those experts who can use several statistical tools and machine learning algorithms to understand and analyze the data related to the particular organization. The average salary of data scientists is in between $95,000 to $165000 per annum as per the survey.
Types of jobs in Data Sciences
If anyone learns data science, then, he gets the opportunity to find several exciting job roles in that domain. Some main job roles are given below:
1. Data Scientist
2. Data Analyst
Types of jobs in Data Sciences
Figure: Data Scientist vs. Data Analyst.
3. Data Architect
4. Data engineer
5. Machine Learning expert
6. Data Administrator
7. Business Analyst
8. Business Intelligence Manager
9. Data Science Generalist
10. Application Architect
11. Infrastructure Architect
12. Enterprise Architect
13. Statistician
Prerequisites of Data Science
Data Science is a vast field that is based on several areas. It is the hottest carrier of the 21st Century. There is an infinite amount of data or information that can be stored, interpreted, and applied for a wide range of purposes. The prerequisite of Data Science is divided into two categories which are given below:
Technical Prerequisites
Many technical skills are required to be a Data Scientist, which are given below:
1. R programming
Any individual needs the depth knowledge of at least one analytical tool like R programming, which is preferred for Data Science. This R programming language is specially designed for Data Science.
We can use the R programming language to solve any problem encountered in Data Science. In fact, 46% of Data scientists are using the R programming language to solve the statistical issues. The R programming language has a steep learning curve.
2. Python language
Python is the most common programming language. It has an essential role in data science along with Java, Perl, C/C++, etc. This programming language is very helpful to the data scientist. The Python is used in data science because of its versatility; we can use it for almost all the steps which are involved in Data Science processes. The python can takes several formats of data, and we can easily import the SQL tables into our codes. It allows the user to create datasets, and the user can find any Data set according to his need on Google.
3. Hadoop Platform
The Hadoop platform is not always required in data science, and this is heavily preferred in some cases. The knowledge and experience of Hive or Pig are the extra points to use the Hadoop platform.
It is also familiar with cloud tools such as Amazon S3, which is very beneficial for data science. As a data scientist, we may encounter a situation where the volume of data we have exceeds the memory of our system, or we need to send the data to the different servers.
We can use the Hadoop to convey data to several points on the system quickly. We can use it for data exploration, data filtration, data sampling, and summarization.
4. SQL Database/ Coding
NoSQL and Hadoop have become a large element of data science. The SQL is the programming language that can help us to carry out the operations like add, delete, and extract data from the database.
It is still expected that the candidate can write and execute the complex queries in SQL. The SQL Database also helps the user to carry out the analytical functions and transform the database structures.
The SQL database is specially designed to help the user to access, communicate, and work on the data. The user needs to be proficient as a data scientist. It gives the user insights when the user uses it for the query in the database.
The SQL database has concise commands which can help the user to save the time and less amount of programming the user needs to perform complicated queries. The learning of SQL will help the user for a better understanding of relational databases and boost the user’s profile as the data scientist.
5. Apache Spark
Apache Spark is becoming the worldwide most popular big data technology. It is a big data computational framework, like Hadoop. The Spark is faster than the Hadoop.
The Hadoop reads and writes to the disk, which makes it slower, but the Spark caches its computations in memory. The Apache spark is specially designed for data science to run its complicated algorithms faster. It also helps in disseminating data processing when we are dealing with a large amount of data to save time.
The Apache sparks are also helping the data scientist to handle the complex unstructured data sets. We can use Apache Spark on one machine or cluster of machines. The Data Scientist can prevent the loss of data with the help of Apache Spark in Data Science.
The Speed of the Apache Spark is its Strength. The user can carry out the analytics from data intake to distributed computing with the help of Apache Spark.
6. Machine Learning and Artificial Intelligence
There is a large number of Data Scientists who are not proficient in machine learning areas and techniques. The machine learning has several fields, such as neural networks, reinforcement learning, adversarial learning, supervised machine learning, decision trees, logistic regression, etc.
These machine learning technical skills will help the user to solve different types of Data Science problems, which are based on the predictions of major organizational outcomes. Data Science is always involved working with a large number of data sets so, the user may want to be familiar with machine learning.
7. Data Visualization
The world of business produces a large amount of data frequently. This produced data needs to be translated into a format that will be easy to comprehend. The people can understand pictures in the forms of charts and graphs more than raw data. There is an idiom “A picture is worth a thousand words” so, we should prefer the graphical representation.
The Data Scientist must be able to visualize the data by the aid of data visualization tools such as ggplot, d3.js, Matplottlib, and Tableau, etc. These data visualization tools will help the user to convert the complex results of projects into that format, which can easily comprehend. Data visualization provides the opportunity to work with the data directly.
8. Unstructured Data
It is very critical that Data Science can work with unstructured data. This type of data defines that content which does not fit into the database tables. The unstructured data includes videos, blog posts, customer reviews, social media posts, video feeds, audio, etc. The sorting of these data is not easy because unstructured data is not streamlined.
Unstructured Data
Figure: Flow of the unstructured data.
Most of the people refer to unstructured data as the “dark analytics” because of its low complexity. As a Data Scientist, the user has the ability to understand and manipulate unstructured data from different platforms.
Non-Technical Prerequisites
1. Intellectual Curiosity
Albert Einstein says that “I have no special talent. I am only passionately curious.” Curiosity can be defined as a desire to acquire more knowledge. If the users have curiosity, then, user can ask more and more questions about the data. Because the data scientists spend 80 percent of the time in discovering and preparing the data.
2. Teamwork
The Data Scientist cannot work alone. He will have to work with the company executives to develop the strategies, work with product managers and designers to make better products. Data scientists will have to work with the marketers to launch the better-converting campaigns, work with the client, and server software developers to create the data pipelines, and improve workflow.
3. Communication skills
Every industry needs an active data scientist who is looking for someone who can translate his technical findings clearly and fluently to the non-technical team, such as marketing or sales departments.
Components of Data Science
There are various types of components exist in Data Science which is given below:
Organizing the data
Organizing the data is the way of planning and execution of the physical storage. The structure of data takes place after applying the best practices of data handling.
Packaging the data
The packaging of data means the creation of prototypes, statistics is applied, and the Visualization is developed. It is related logically as well as aesthetically modifying and combining the data in the presentable form.
Deliver the data
The Delivery of data is related to that story, which is narrated, and value is received after the process. This delivers data to make sure that the final output has been delivered to the concerned person.
Data Analysis
The Data analysis is like quizzical activity. It is the process of inspecting, transforming, and modeling the data with the discovering of useful information or data.
Data integration is the precursor of Data analysis, and it is closely linked to data visualization and data dissemination. The data analysis breaks out the macro picture of data into the micro image. It helps the user to identify new or unusual patterns and grasp difficult concepts.
Data Analytics
Data Analytics is the technique of data analysis. It can examine the data sets and concludes the information. Data analytics is used widely in the commercial industry.
Data Mining
Data mining is a process that helps the industries in turning their raw data into a useful and informative form. It can increase profitability. Data Mining is an advanced type of data analytics.
It uses that type of software which looks for the patterns in the large batches of data. The data mining helps users to enhance the quality of customer relations with the help of ensuring and giving the best quality of products.
Big Data
Big Data referred to the massive, high-volume, structured, or unstructured data. It is the processing that enables enhanced insight, decision making, and process automation. Big data is an asset for organizations. It can control the online reputation via the tools which can do sentimental analysis.
Machine Learning
Machine Learning is a subdivision of artificial intelligence or an application. It is used in multi-dimensional and multi-variety of data, which is compatible in the dynamic environments. Machine learning simplifies the Time-Intensive Documentation by using the data entry.
Statistics
The statistics are a vast field of data science. It is a way to analyze numerical data. It is used to find meaningful insights from the numerical data.
Domain Expertise
The domain expertise combines data science together. It is related to the specialized knowledge or skills of a particular field. There are several fields in data science for which we require domain expertise.
Data Engineering
Data Engineering is a data science aspect that focuses on the practical applications of data collection and analysis.
Visualization
The data visualization refers to the techniques which are used to communicate the data or information by encoding it as the visual objects contained in the graphics. It includes the graphs, charts, mind maps, infographics, and other and other visuals to help convey key data.
Advanced computing
Advanced computing is the heavy lifting of data science. It is related to the designing, writing, debugging, and maintaining the source code of the computer programs.
Mathematics
Mathematics is a very critical part of data science. It is related to the study of quantity, structure, space, and changes. The good knowledge of mathematics is essential for data scientists.
Tools of Data Science
The Data scientist is responsible for extracting, manipulating, pre-processing, and generate the predictions out of data. So, they require statistical tools and programming languages. These tools are used by the data scientist to carry out their data operations.
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