Data science is a deep study of the massive amount of data, which involves extracting meaningful insights from raw, structured, and unstructured data that is processed using the scientific method, different technologies, and algorithms.
It is a multidisciplinary field that uses tools and techniques to manipulate the data so that you can find something new and meaningful.
Data science uses the most powerful hardware, programming systems, and most efficient algorithms to solve the data related problems. It is the future of artificial intelligence.
In short, we can say that data science is all about:
• Asking the correct questions and analyzing the raw data.
• Modeling the data using various complex and efficient algorithms.
• Visualizing the data to get a better perspective.
• Understanding the data to make better decisions and finding the final result.

Example:
Let suppose we want to travel from station A to station B by car. Now, we need to take some decisions such as which route will be the best route to reach faster at the location, in which route there will be no traffic jam, and which will be cost-effective. All these decision factors will act as input data, and we will get an appropriate answer from these decisions, so this analysis of data is called the data analysis, which is a part of data science.

Need for Data Science:

Some years ago, data was less and mostly available in a structured form, which could be easily stored in excel sheets, and processed using BI tools.
But in today’s world, data is becoming so vast, i.e., approximately 2.5 quintals bytes of data is generating on every day, which led to data explosion. It is estimated as per researches, that by 2020, 1.7 MB of data will be created at every single second, by a single person on earth. Every Company requires data to work, grow, and improve their businesses.
Now, handling of such huge amount of data is a challenging task for every organization. So to handle, process, and analysis of this, we required some complex, powerful, and efficient algorithms and technology, and that technology came into existence as data Science. Following are some main reasons for using data science technology:
• With the help of data science technology, we can convert the massive amount of raw and unstructured data into meaningful insights.
• Data science technology is opting by various companies, whether it is a big brand or a startup. Google, Amazon, Netflix, etc, which handle the huge amount of data, are using data science algorithms for better customer experience.
• Data science is working for automating transportation such as creating a self-driving car, which is the future of transportation.
• Data science can help in different predictions such as various survey, elections, flight ticket confirmation, etc.

Data science Jobs:

As per various surveys, data scientist job is becoming the most demanding Job of the 21st century due to increasing demands for data science. Some people also called it “the hottest job title of the 21st century”. Data scientists are the experts who can use various statistical tools and machine learning algorithms to understand and analyze the data.
The average salary range for data scientist will be approximately $95,000 to $ 165,000 per annum, and as per different researches, about 11.5 millions of job will be created by the year 2026.
Types of Data Science Job
If you learn data science, then you get the opportunity to find the various exciting job roles in this domain. The main job roles are given below:
1. Data Scientist
2. Data Analyst
3. Machine learning expert
4. Data engineer
5. Data Architect
6. Data Administrator
7. Business Analyst
8. Business Intelligence Manager
Below is the explanation of some critical job titles of data science.
1. Data Analyst:
Data analyst is an individual, who performs mining of huge amount of data, models the data, looks for patterns, relationship, trends, and so on. At the end of the day, he comes up with visualization and reporting for analyzing the data for decision making and problem-solving process.
Skill required: For becoming a data analyst, you must get a good background in mathematics, business intelligence, data mining, and basic knowledge of statistics. You should also be familiar with some computer languages and tools such as MATLAB, Python, SQL, Hive, Pig, Excel, SAS, R, JS, Spark, etc.
2. Machine Learning Expert:
The machine learning expert is the one who works with various machine learning algorithms used in data science such as regression, clustering, classification, decision tree, random forest, etc.
Skill Required: Computer programming languages such as Python, C++, R, Java, and Hadoop. You should also have an understanding of various algorithms, problem-solving analytical skill, probability, and statistics.
3. Data Engineer:
A data engineer works with massive amount of data and responsible for building and maintaining the data architecture of a data science project. Data engineer also works for the creation of data set processes used in modeling, mining, acquisition, and verification.
Skill required: Data engineer must have depth knowledge of SQL, MongoDB, Cassandra, HBase, Apache Spark, Hive, MapReduce, with language knowledge of Python, C/C++, Java, Perl, etc.
4. Data Scientist:
A data scientist is a professional who works with an enormous amount of data to come up with compelling business insights through the deployment of various tools, techniques, methodologies, algorithms, etc.
Skill required: To become a data scientist, one should have technical language skills such as R, SAS, SQL, Python, Hive, Pig, Apache spark, MATLAB. Data scientists must have an understanding of Statistics, Mathematics, visualization, and communication skills.

Prerequisite for Data Science

Non-Technical Prerequisite:
• Curiosity: To learn data science, one must have curiosities. When you have curiosity and ask various questions, then you can understand the business problem easily.
• Critical Thinking: It is also required for a data scientist so that you can find multiple new ways to solve the problem with efficiency.
• Communication skills: Communication skills are most important for a data scientist because after solving a business problem, you need to communicate it with the team.
Technical Prerequisite:
• Machine learning: To understand data science, one needs to understand the concept of machine learning. Data science uses machine learning algorithms to solve various problems.
• Mathematical modeling: Mathematical modeling is required to make fast mathematical calculations and predictions from the available data.
• Statistics: Basic understanding of statistics is required, such as mean, median, or standard deviation. It is needed to extract knowledge and obtain better results from the data.
• Computer programming: For data science, knowledge of at least one programming language is required. R, Python, Spark are some required computer programming languages for data science.
• Databases: The depth understanding of Databases such as SQL, is essential for data science to get the data and to work with data.

Difference between BI and Data Science

BI stands for business intelligence, which is also used for data analysis of business information: Below are some differences between BI and

Criterion	Business intelligence	Data science
Data Source	Business intelligence deals with structured data, e.g., data warehouse.	Data science deals with structured and unstructured data, e.g., weblogs, feedback, etc.
Method	Analytical (historical data)	Scientific (goes deeper to know the reason for the data report)
Skills	Statistics and Visualization are the two skills required for business intelligence.	Statistics, Visualization, and Machine learning are the required skills for data science.
Focus	Business intelligence focuses on both Past and present data	Data science focuses on past data, present data, and also future predictions.

Data Science Components:

The main components of Data Science are given below:
1. Statistics: Statistics is one of the most important components of data science. Statistics is a way to collect and analyze the numerical data in a large amount and finding meaningful insights from it.
2. Domain Expertise: In data science, domain expertise binds data science together. Domain expertise means specialized knowledge or skills of a particular area. In data science, there are various areas for which we need domain experts.
3. Data engineering: Data engineering is a part of data science, which involves acquiring, storing, retrieving, and transforming the data. Data engineering also includes metadata (data about data) to the data.
4. Visualization: Data visualization is meant by representing data in a visual context so that people can easily understand the significance of data. Data visualization makes it easy to access the huge amount of data in visuals.
5. Advanced computing: Heavy lifting of data science is advanced computing. Advanced computing involves designing, writing, debugging, and maintaining the source code of computer programs.

6. Mathematics: Mathematics is the critical part of data science. Mathematics involves the study of quantity, structure, space, and changes. For a data scientist, knowledge of good mathematics is essential.
7. Machine learning: Machine learning is backbone of data science. Machine learning is all about to provide training to a machine so that it can act as a human brain. In data science, we use various machine learning algorithms to solve the problems.

Tools for Data Science

Following are some tools required for data science:
• Data Analysis tools: R, Python, Statistics, SAS, Jupyter, R Studio, MATLAB, Excel, RapidMiner.
• Data Warehousing: ETL, SQL, Hadoop, Informatica/Talend, AWS Redshift
• Data Visualization tools: R, Jupyter, Tableau, Cognos.
• Machine learning tools: Spark, Mahout, Azure ML studio.

Machine learning in Data Science

To become a data scientist, one should also be aware of machine learning and its algorithms, as in data science, there are various machine learning algorithms which are broadly being used. Following are the name of some machine learning algorithms used in data science:
• Regression
• Decision tree
• Clustering
• Principal component analysis
• Support vector machines
• Naive Bayes
• Artificial neural network
• Apriori
We will provide you some brief introduction for few of the important algorithms here,
1. Linear Regression Algorithm: Linear regression is the most popular machine learning algorithm based on supervised learning. This algorithm work on regression, which is a method of modeling target values based on independent variables. It represents the form of the linear equation, which has a relationship between the set of inputs and predictive output. This algorithm is mostly used in forecasting and predictions. Since it shows the linear relationship between input and output variable, hence it is called linear regression.

The below equation can describe the relationship between x and y variables:
1. Y= mx+c
Where, y= Dependent variable
X= independent variable
M= slope
C= intercept.
2. Decision Tree: Decision Tree algorithm is another machine learning algorithm, which belongs to the supervised learning algorithm. This is one of the most popular machine learning algorithms. It can be used for both classification and regression problems.
In the decision tree algorithm, we can solve the problem, by using tree representation in which, each node represents a feature, each branch represents a decision, and each leaf represents the outcome.
Following is the example for a Job offer problem:

In the decision tree, we start from the root of the tree and compare the values of the root attribute with record attribute. On the basis of this comparison, we follow the branch as per the value and then move to the next node. We continue comparing these values until we reach the leaf node with predicated class value.
3. K-Means Clustering: K-means clustering is one of the most popular algorithms of machine learning, which belongs to the unsupervised learning algorithm. It solves the clustering problem.
If we are given a data set of items, with certain features and values, and we need to categorize those set of items into groups, so such type of problems can be solved using k-means clustering algorithm.
K-means clustering algorithm aims at minimizing an objective function, which known as squared error function, and it is given as:

Where, J(V) => Objective function
‘||xi – vj||’ => Euclidean distance between xi and vj.
ci’ => Number of data points in ith cluster.
C => Number of clusters.

How to solve a problem in Data Science using Machine learning algorithms?

Now, let’s understand what are the most common types of problems occurred in data science and what is the approach to solving the problems. So in data science, problems are solved using algorithms, and below is the diagram representation for applicable algorithms for possible questions:

Is this A or B?:
We can refer to this type of problem which has only two fixed solutions such as Yes or No, 1 or 0, may or may not. And this type of problems can be solved using classification algorithms.
Is this different?:
We can refer to this type of question which belongs to various patterns, and we need to find odd from them. Such type of problems can be solved using Anomaly Detection Algorithms.
How much or how many?
The other type of problem occurs which ask for numerical values or figures such as what is the time today, what will be the temperature today, can be solved using regression algorithms.
How is this organized?
Now if you have a problem which needs to deal with the organization of data, then it can be solved using clustering algorithms.
Clustering algorithm organizes and groups the data based on features, colors, or other common characteristics.

Data Science Lifecycle

The life-cycle of data science is explained as below diagram.

The main phases of data science life cycle are given below:
1. Discovery: The first phase is discovery, which involves asking the right questions. When you start any data science project, you need to determine what are the basic requirements, priorities, and project budget. In this phase, we need to determine all the requirements of the project such as the number of people, technology, time, data, an end goal, and then we can frame the business problem on first hypothesis level.
2. Data preparation: Data preparation is also known as Data Munging. In this phase, we need to perform the following tasks:
• Data cleaning
• Data Reduction
• Data integration
• Data transformation,
After performing all the above tasks, we can easily use this data for our further processes.
3. Model Planning: In this phase, we need to determine the various methods and techniques to establish the relation between input variables. We will apply Exploratory data analytics (EDA) by using various statistical formula and visualization tools to understand the relations between variable and to see what data can inform us. Common tools used for model planning are:
• SQL Analysis Services
• R
• SAS
• Python
4. Model-building: In this phase, the process of model building starts. We will create datasets for training and testing purpose. We will apply different techniques such as association, classification, and clustering, to build the model.
Following are some common Model building tools:
• SAS Enterprise Miner
• WEKA
• SPCS Modeler
• MATLAB
5. Operationalize: In this phase, we will deliver the final reports of the project, along with briefings, code, and technical documents. This phase provides you a clear overview of complete project performance and other components on a small scale before the full deployment.
6. Communicate results: In this phase, we will check if we reach the goal, which we have set on the initial phase. We will communicate the findings and final result with the business team.

Applications of Data Science:

• Image recognition and speech recognition:
Data science is currently using for Image and speech recognition. When you upload an image on Facebook and start getting the suggestion to tag to your friends. This automatic tagging suggestion uses image recognition algorithm, which is part of data science.
When you say something using, “Ok Google, Siri, Cortana”, etc., and these devices respond as per voice control, so this is possible with speech recognition algorithm.
• Gaming world:
In the gaming world, the use of Machine learning algorithms is increasing day by day. EA Sports, Sony, Nintendo, are widely using data science for enhancing user experience.
• Internet search:
When we want to search for something on the internet, then we use different types of search engines such as Google, Yahoo, Bing, Ask, etc. All these search engines use the data science technology to make the search experience better, and you can get a search result with a fraction of seconds.
• Transport:
Transport industries also using data science technology to create self-driving cars. With self-driving cars, it will be easy to reduce the number of road accidents.
• Healthcare:
In the healthcare sector, data science is providing lots of benefits. Data science is being used for tumor detection, drug discovery, medical image analysis, virtual medical bots, etc.
• Recommendation systems:
Most of the companies, such as Amazon, Netflix, Google Play, etc., are using data science technology for making a better user experience with personalized recommendations. Such as, when you search for something on Amazon, and you started getting suggestions for similar products, so this is because of data science technology.
• Risk detection:
Finance industries always had an issue of fraud and risk of losses, but with the help of data science, this can be rescued.
Most of the finance companies are looking for the data scientist to avoid risk and any type of losses with an increase in customer satisfaction.
So, this brings us to the end of blog. This Tecklearn ‘Overview of Data Science’ blog helps you with commonly asked questions if you are looking out for a job in Data Science. If you wish to learn Data Science and build a career in Data Science domain, then check out our interactive, Data Science using R Language Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

https://www.tecklearn.com/course/data-science-training-using-r-language/

Data Science using R Language Training

About the Course

Tecklearn’s Data Science using R Language Training develops knowledge and skills to visualize, transform, and model data in R language. It helps you to master the Data Science with R concepts such as data visualization, data manipulation, machine learning algorithms, charts, hypothesis testing, etc. through industry use cases, and real-time examples. Data Science course certification training lets you master data analysis, R statistical computing, connecting R with Hadoop framework, Machine Learning algorithms, time-series analysis, K-Means Clustering, Naïve Bayes, business analytics and more. This course will help you gain hands-on experience in deploying Recommender using R, Evaluation, Data Transformation etc.

Why Should you take Data Science Using R Training?

• The Average salary of a Data Scientist in R is $123k per annum – Glassdoor.com
• A recent market study shows that the Data Analytics Market is expected to grow at a CAGR of 30.08% from 2020 to 2023, which would equate to $77.6 billion.
• IBM, Amazon, Apple, Google, Facebook, Microsoft, Oracle & other MNCs worldwide are using data science for their Data analysis.

What you will Learn in this Course?

Introduction to Data Science
• Need for Data Science
• What is Data Science
• Life Cycle of Data Science
• Applications of Data Science
• Introduction to Big Data
• Introduction to Machine Learning
• Introduction to Deep Learning
• Introduction to R&R-Studio
• Project Based Data Science
Introduction to R
• Introduction to R
• Data Exploration
• Operators in R
• Inbuilt Functions in R
• Flow Control Statements & User Defined Functions
• Data Structures in R
Data Manipulation
• Need for Data Manipulation
• Introduction to dplyr package
• Select (), filter(), mutate(), sample_n(), sample_frac() & count() functions
• Getting summarized results with the summarise() function,
• Combining different functions with the pipe operator
• Implementing sql like operations with sqldf()
Visualization of Data
• Loading different types of datasets in R
• Arranging the data
• Plotting the graphs
Introduction to Statistics
• Types of Data
• Probability
• Correlation and Co-variance
• Hypothesis Testing
• Standardization and Normalization
Introduction to Machine Learning
• What is Machine Learning?
• Machine Learning Use-Cases
• Machine Learning Process Flow
• Machine Learning Categories
• Supervised Learning algorithm: Linear Regression and Logistic Regression
Logistic Regression
• Intro to Logistic Regression
• Simple Logistic Regression in R
• Multiple Logistic Regression in R
• Confusion Matrix
• ROC Curve
Classification Techniques
• What are classification and its use cases?
• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Perfect Decision Tree
• Confusion Matrix
• What is Random Forest?
• What is Naive Bayes?
• Support Vector Machine: Classification
Decision Tree
• Decision Tree in R
• Information Gain
• Gini Index
• Pruning
Recommender Engines
• What is Association Rules & its use cases?
• What is Recommendation Engine & it’s working?
• Types of Recommendations
• User-Based Recommendation
• Item-Based Recommendation
• Difference: User-Based and Item-Based Recommendation
• Recommendation use cases
Time Series Analysis
• What is Time Series data?
• Time Series variables
• Different components of Time Series data
• Visualize the data to identify Time Series Components
• Implement ARIMA model for forecasting
• Exponential smoothing models
• Identifying different time series scenario based on which different Exponential Smoothing model can be applied

Got a question for us? Please mention it in the comments section and we will get back to you.

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