Data Science Tutorial – Learn Data Science from Experts
Want to start your career as a Data Scientist, but don’t know where to start? You are at the right place! Hey Guys, welcome to this awesome Data Science Tutorial blog, it will give you a kick start into the data science world. To get in-depth knowledge on Data Science, you can enroll for live Data Science Certification Training by ITCourCes with 24/7 support and lifetime access. Let’s look at what we will be learning today.
Data Science is one of the hottest jobs of the 21st century with an average salary of $123,000 per year. According to LinkedIn, the Data Scientist job profile is among the top 10 jobs in the United States. As per McKinsey’s reports, the United States alone faces a job shortage of 1.5 million Data Scientists. So, Data Science is a hot cake now and every single soul on the planet wants to get a piece of it. Become a Master of Data Science by going through this Data Science Course. So, let’s get started with a Data Science Tutorial!
Here, are significant advantages of using Data Analytics Technology:
DATA SCIENCE is the area of study which involves extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes. It helps you to discover hidden patterns from the raw data. The term Data Science has emerged because of the evolution of mathematical statistics, data analysis, and big data.
Data Science is an interdisciplinary field that allows you to extract knowledge from structured or unstructured data. Data science enables you to translate a business problem into a research project and then translate it back into a practical solution.
As you can see in the image, a Data Scientist is the master of all trades! He should be proficient in maths, he should be acing the Business field and should have great Computer Science skills as well. Scared? Don’t be. Though you need to be good in all these fields, even if you aren’t, you’re not alone! There is no such thing as “a complete data scientist”. If we talk about working in a corporate environment, the work is distributed among teams, wherein each team has their own expertise. But the thing is, you should be proficient in at least one of these fields. Also, even if these skills are new to you, chill! It may take time, but these skills can be developed, and believe me it would be worth the time you will be investing. Why? Well, let’s look at the job trends.
Well, the graph says it all, not only is there a lot of job openings for a data scientist, but the jobs are well-paid too! And no, our blog will not cover the salary figures, go google!
Well, we now know, learning data science actually makes sense, not only because it is very useful, but also you have a great career in it in the near future.
Let’s start our journey in learning data science now and begin with,
Most prominent Data Scientist job titles are:
Let’s learn what each role entails in detail:
A Data Scientist is a professional who manages enormous amounts of data to come up with compelling business visions by using various tools, techniques, methodologies, algorithms, etc.
R, SAS, Python, SQL, Hive, Matlab, Pig, Spark
The role of a data engineer is working with large amounts of data. He develops, constructs, tests, and maintains architectures like large scale processing systems and databases.
SQL, Hive, R, SAS, Matlab, Python, Java, Ruby, C + +, and Perl
A data analyst is responsible for mining vast amounts of data. He or she will look for relationships, patterns, trends in data. Later he or she will deliver compelling reporting and visualization for analyzing the data to make the most viable business decisions.
R, Python, HTML, JS, C, C+ + , SQL
The statistician collects, analyses, and understands qualitative and quantitative data by using statistical theories and methods.
SQL, R, Matlab, Tableau, Python, Perl, Spark, and Hive
Data admin should ensure that the database is accessible to all relevant users. He also makes sure that it is performing correctly and is being kept safe from hacking.
Ruby on Rails, SQL, Java, C#, and Python
This professional needs to improve business processes. He/she acts as an intermediary between the business executive team and the IT department.
SQL, Tableau, Power BI and, Python
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 that are broadly being used. Following are the name of some machine learning algorithms used in data science:
We will provide you some brief introduction for a 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 works 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 variables, hence it is called linear regression.
Where, y= Dependent variable
X= independent variable
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 the 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.
If we are given a data set of items, with certain features and values, and we need to categorize those sets of items into groups, such types of problems can be solved using the 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 the ith cluster.
C => Number of clusters.
So now, let’s discuss how one should approach a problem and solve it with data science. Problems in Data Science are solved using Algorithms. But, the biggest thing to judge is which algorithm to use and when to use it?
Basically there are 5 kinds of problems which you can face in data science.
Let’s address each of these questions and the associated algorithms one by one:
Is this A or B?
With this question, we are referring to problems that have a categorical answer, as in problems that have a fixed solution, the answer could either be a yes or a no, 1 or 0, interested, maybe or not interested.
Here, you cannot say you would want a coke! Since the question only offers tea or coffee, and hence you may answer one of these only.
When we have only two types of answers i.e yes or no, 1 or 0, it is called 2 – Class Classification. With more than two options, it is called Multi-Class Classification.
Concluding, whenever you come across questions, the answer to which is categorical, in Data Science you will be solving these problems using Classification Algorithms.
The next problem in this Data Science Tutorial, that you may come across, maybe something like this,
Is this weird?
Questions like these deal with patterns and can be solved using Anomaly Detection algorithms.
Try associating the problem “is this weird?” to this diagram,
What is weird in the above pattern? The red guy, isn’t it?
Whenever there is a break in the pattern, the algorithm flags that particular event for us to review. A real-world application of this algorithm has been implemented by Credit Card companies where any unusual transaction by a user is flagged for review. Hence implementing security and reducing human effort on surveillance.
Let’s look at the next problem in this Data Science Tutorial, don’t be scared, deal with maths!
How much or How many?
Those of you, who don’t like maths, be relieved! Regression algorithms are here!
So, whenever there is a problem that may ask for figures or numerical values, we solve it using Regression Algorithms.
What will be the temperature for tomorrow?
Since we expect a numeric value in the response to this problem, we will solve it using Regression Algorithms.
Moving along in this Data Science Tutorial, let’s discuss the next algorithm,
How is this organized?
Say you have some data, now you don’t have any idea how to make sense out of this data. Hence the question, how is this organized?
Well, you can solve it using clustering algorithms. How do they solve these problems? Let’s see:
Clustering algorithms group the data in terms of characteristics that are common. For example in the above diagram, the dots are organized based on colors. Similarly, be it any data, clustering algorithms try to apprehend what is common between them and hence “clusters” them together.
The next and final kind of problem in this Data Science Tutorial, that you may encounter is,
What should I do next?
Whenever you encounter a problem, wherein your computer has to make a decision based on the training that you have given it, it involves Reinforcement Algorithms.
Your temperature control system, when it has to decide whether it should lower the temperature of the room, or increase it.
What will you analyze on? Data, right? You need a lot of data which can be analyzed, this data is fed to your algorithms or analytical tools. You get this data from various researches conducted in the past.
R is an open-source programming language and software environment for statistical computing and graphics that is supported by the R foundation. The R language is used in an IDE called R Studio.
Why is it used?
R Studio was sufficient for analysis, until our datasets became huge, also unstructured at the same time. This type of data was called Big Data.
Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.
Now to tame this data, we had to come up with a tool, because no traditional software could handle this kind of data, and hence we came up with Hadoop.
Hadoop is a framework that helps us to store and process large datasets in parallel and in a distribution fashion.
Let’s focus on the store and process part of Hadoop.
The storage part in Hadoop is handled by HDFS i.e Hadoop Distributed File System. It provides high availability across a distributed ecosystem. The way it functions is like this, it breaks the incoming information into chunks, and distributes them to different nodes in a cluster, allowing distributed storage.
MapReduce is the heart of Hadoop processing. The algorithms do two important tasks, map and reduce. The mappers break the task into smaller tasks that are processed parallel. Once, all the mappers do their share of work, they aggregate their results, and then these results are reduced to a simpler value by the Reduce process.
The life-cycle of data science is explained as below diagram.
The main phases of data science life cycle are given below:
After performing all the above tasks, we can easily use this data for our further processes.
Following are some common Model building tools:
Structured Data. Mostly SQL, but some time Data Warehouse)
Structured and Unstructured data. Like logs, SQL, NoSQL, or text
Statistics & Visualization
Statistics, Machine Learning, and Graph
Past & Present
Analysis & Neuro-linguistic Programming
Pentaho. Microsoft Bl, QlikView,
According to the Harvard Business Review, Data scientists are the best jobs of the 21st century. Today, most organizations are willing to pay high salaries for professionals with the right skills. Thus, you can accelerate your career, get promising jobs, and take your career to the next level by learning Data Science.
Data Scientist’s typical job is to identify data analytics problems, collect structured and unstructured data from multiple sources, clean/verify data, apply models/algorithms to mine Big Data, analyze and interpret data, and communicate the findings.
Data scientists need knowledge of statistics and programming. You will be happy to know that ITCourCes offers one of the best Data science courses in the country to help you learn about Data Science, its tools and methods. You will also participate in many hands-on projects to learn how to deal with industry-specific solutions.
Everyone can learn about data science. In general, learners who want to work as data scientists or professionals belonging to Big Data, business intelligence, information architecture, and machine learning, opt for learning Data Science.
Many people want to learn Data Science, but only a few become Data Scientists because learning Data Science is not easy. It requires a combination of skills/knowledge, such as Algorithms, Python, SQL. However, learning Data science can be easy if you have access to the right Data Science tutorial.
Yes, you can become a self-learning data scientist. However, it requires commitment and planning. This data science tutorial will provide you with what you need to learn (Basic Data Science Course). In addition, this field is interdisciplinary, so you need to focus on each topic. If you are unable to self-learn, you can turn to IT CourCes for guidance.
The average salary of Data Scientists in the US is around $120,000 and the average salary in India is close to INR 10,00,000.
Today every company hires data scientists. Some of the top companies hiring data scientists include IBM, Google, Amazon, Oracle, Microsoft, Apple, Facebook, Walmart, Visa, Bank of America, and others.
What is Data Science?: The simplest Data Science meaning would be, applying some scientific skills on top of data so that we can make this data talk to us. Now, what we exactly mean by ‘applying scientific skills on top of data’? Well, to put it precisely, Data Science is an umbrella term that encompasses multiple skills and scientific techniques. Techniques
Data Science Command Line Tools: Here, we are going to look at the most convenient and common Data Science Command tools for quick analysis of data. Watch this Data Science Tutorial video [videothumb class=”col-md-12″ id=”pcGePSWo2ew” alt=”Data Science Tutorial” title=”Data Science Tutorial”] alias It defines or display aliases. It is a Bash built-in. $ help alias $ alias ll=’ls -alF’ bash
Machine Learning in Data Science: It is a process or collection of rules or set to complete a task. It is one of the primary concepts in, or building blocks of, computer science: the basis of the design of elegant and efficient code, data processing and preparation, and software engineering. We have the perfect professional Data Science Training Course for
What is Data Acquisition?: There are many ways to get a dataset like configuring an API, internet, database, etc. To convert binary data into useful data, we need to perform certain tasks which includes-Decompress files, Querying relational database, etc. It is very much important to track the origin of the database and check whether that data is up to date
Techniques for Scrubbing or Cleaning Data in Data Science: As we know the obtained data has inconsistencies, errors, weird characters, missing values, or different problems. In this situation, you have to scrub or clean the data before using this data. We have the perfect professional Data Science Training Course for you! So for scrubbing the data in Data Science.
Data Visualization in R programming: Here we will be using the R programming language to visualize data. It is very important to visualize the result in a graphical format, to analyze the obtained output. Apart from that, we will be deriving statistics to get all the unique values, identifiers, factors, and continuous variables. We can check the overall result through
Data Modelling Concepts in Data Science: To predict something useful from the datasets, we need to implement machine learning algorithms. Since, there are many types of algorithms like SVM Algorithm in Python, Bayes, Regression, etc. We will be using four algorithms- Dimensionality Reduction It is a very important algorithm as it is unsupervised i.e. it can implement raw data
Data Extraction in R: In data extraction, the initial step is data pre-processing or data cleaning. In data cleaning, the task is to transform the dataset into a basic form that makes it easy to work with. One characteristic of a clean/tidy dataset is that it has one observation per row and one variable per column.
The time is ripe to up-skill in Data Science and Big Data Analytics to take advantage of the Data Science career opportunities that come your way. This brings us to the end of the Data Science tutorial blog. I hope this blog was informative and added value to you. Now is the time to enter the Data Science world and become a successful Data Scientist.
Got a question for us in the Data Science Tutorial? Please mention it in the comments section and we will get back to you.