Contact Us

Hide

100,000 Online Courses

Explore a variety of fresh topics

Expert Instruction

Find the right instructor for you

Lifetime Access

Learn on your schedule

Student are Viewing

Enroll, Learn, Grow, Repeat! Get ready to achieve your learning goals with Gyansetu

Recent Additions

What our students have to say

View All

Popular Instructors

View All

Gyansetu Advantages

Logic building that brings real transformation

Problem solving is the key essential need of any programmer. A good coder has strong analytical thinking, logical and mathematical skills.

Instructor-led Classroom training experience

Take live, structured classroom & online classes from the convenience of wherever, with instant, one-on-one help.

Faculty having exposure with top companies

We deliver training by experts from top companies like Microsoft, Amazon, American Express, Mckinsey, Barclays & more.

Career Support

We connect our student to software companies via our placement assistance program.

Master Course

Blogs

Top 10 Machine Learning Frameworks {Complete Guide}

There has been exponential growth in the field of Machine Learning/Artificial Intelligence and it has created a boom in the technology field. According to the reports, it has created millions of jobs and has led to the evolution of many Machine Learning frameworks. Today we will look into some of these most popularly used Machine Learning Frameworks. What is Machine Learning? Machine Learning is a part of Artificial Intelligence, consisting of algorithms that work on outputs generated from previous experiences. The more input parameters (more experiences) they get, the better the results can be expected. The uniqueness of these algorithms is that they don’t require human intervention. But, the human brain is always required to understand which machine learning algorithm will fit best for a particular situation. Data sets (inputs) are divided into training and testing sets. Machine learning algorithms work on the training data sets in order to build models for prediction and decision making. Some applications of machine learning are computer vision, collaborative filtering, natural language processing, spam filtering, etc. What is a Machine Learning Framework? ML models can be easily developed with the help of Machine Learning frameworks, without knowing the ML Algorithm. Python is the most widely used language in machine learning and so most of the ML frameworks are built for programming in Python language.  Machine Learning with R Programming is also widely used in Data Science fields.  Read More:- IS PYTHON ENOUGH FOR MACHINE LEARNING 10 Machine Learning Frameworks widely used are:- Tensorflow Keras Scikit-learn Theano Amazon SageMaker Spark ML Microsoft Cognitive Toolkit (CNTK) H20 Caffe Torch Tensorflow Google’s Tensorflow is the most popularly used framework for machine learning/deep learning. It is an open-source platform for machine learning. ML Applications can be easily built and deployed using Tensorflow. It contains a broad range of multiple libraries, tools and community resources that greatly enriches the developers' experience and makes the development easy. Keras Keras is a neural network library built on top of TensorFlow to make Machine Learning modeling easier. It simplifies some of the coding steps, like offering all-in-one models, and can use the same Keras code to run on a CPU or a GPU. Keras is a tool designed for human beings, not machines. Its features and working like load balancing, steady and easy APIs, minimizing the user inputs to execute use cases and providing simple, clear and actionable error messages greatly benefits the developer's coding experience. Scikit-Learn Scikit-Learn is the most popular and frequently in-use ML library. It features various algorithms that are designed to work efficiently with the Numpy and Scipy. These algorithms comprise regression, classification and clustering including k-means, random forests, gradient boosting, DBSCAN, SVM (Support Vector Machine).  Theano Theano is a Python library built on top of Numpy Library. It is primarily used to evaluate multi-dimensional arrays and expressions that require mathematical manipulation. Mathematical expressions are compiled to execute properly on CPU/GPU architectures and are presented in a Numpy-Esque syntax. Amazon SageMaker Amazon SageMaker was released on 29 November 2017 that provides an integrated development environment for machine learning models. AWS provides this Machine Learning service for applications such as Computer Vision, Collaborative Filtering, Image and Video Analytics, Forecasting, Text Analytics, etc. You can choose Amazon SageMaker to build, train, and deploy machine learning models on the cloud. All this can be automated using Amazon SageMaker Autopilot which has capabilities to automate machine learning models. Amazon SageMaker allows you to create ML algorithms from scratch as it is connected to Tensorflow and Apache MXNet. Spark ML Lib Apache Spark provides an interface to programmers for complete clusters. It is a widely popularly used open-source cluster-computing framework. Spark Core is the base of the Apache Spark. It provides in-memory computation to increase the speed and also allows the parallel processing of big data. Spark SQL works more efficiently and easily to optimize the structured data set. It is the distributed framework that works on structured data processing. Spark Streaming is one of the widely used live streaming & high scalable processing that ensures fault-tolerant solutions. It works on dividing the live dataset into multiple small batches before processing. Spark MLib is Spark's machine learning library which has very advanced algorithms, highly scalable and high-speed functionalities. It consists of algorithms like clustering, regression, classification, dimensionality reduction, collaborative filtering.  Read more:- BIG DATA ANALYTICS USING MACHINE LEARNING, SPARK IS HOTTEST JOB MARKET THIS YEAR Microsoft Cognitive Toolkit (CNTK) The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit developed by Microsoft Research. With the help of CNTK, users can easily combine models like recurrent neural networks (RNNs), convolutional neural networks (CNNs) and DNNs. It has explained neural networks as a series of computational steps. Automatic parallelization and differentiation across servers and GPUs can be implemented with the help of stochastic gradient descent (SGD, error backpropagation) learning. Multiple GPUs and servers are used to provide parallelization across the backend. H20 H20 is a decision-making artificial intelligence tool that provides business-oriented insights to the users. It is an open-source machine learning platform used for fraud analytics, healthcare, risk analytics, modeling, insurance analytics, financial analytics, and customer intelligence.  Read Our Blog:- WHAT IS NATURAL LANGUAGE PROCESSING? INTRO TO NLP IN MACHINE LEARNING Caffe Caffe is provided by the Berkeley Vision and Learning Center (BVLC) and by network donors. Caffe Framework is used by Google’s DeepDream. This popular learning structure is a BSD-authorized C++ library with Python Interface. It is made with the best quality and high speed. Torch Torch framework provides support for ML algorithms to GPUs first. It is built on an easy and fast scripting language LuaJIT and an underlying C/CUDA implementation, which makes it easy to use and efficient. The goal of this framework is to have maximum flexibility and speed in building your ML algorithms. If you want to Learn ML then you can Join Gyansetu's Machine Learning Training Course. 

5 Best Big Data Tools You Must Know

Data – a four-lettered word that makes the world go round. As per research conducted by DOMO, “Over 2.5 quintillion bytes of data are created every single day, and it’s only going to grow from there. By 2020, it’s estimated that 1.7MB of data will be created every second for every person on earth.”   An increase in the number of users of the internet and influx of data has also made things simpler for businesses. An economic environment is made up of transactions between consumers and businesses. Similarly, a business organization is nothing without its human resource. Interaction between these resources is streamlined and made simpler with the help of data analysis. As rightly said by Atul Butte in Stanford, “Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world.” If these sentences do not make any sense to you, let us tell you how data can help businesses – With the help of collected data of consumers, your business can redesign its marketing strategies to achieve better results. Through data, your business can hire better resources with the help of online tools. Data can help your business predict future trends and modify your plans as per them. Data can also help in personalizing the experience of your consumers, thereby, increasing their satisfaction. However, the question which arises now is that if there’s a sea of data being generated every minute, how does a business reach a decision with its help? This is where Big Data comes in. WHAT IS BIG DATA? In simple words, big data is data generated from various sources and through different formats. Thanks to the amount of information available on internet, the accumulated data is growing exponentially every day.  Big data is primarily defined through Vs. Let us tell you what they are – VELOCITY: Data flows in higher velocity and speed than in earlier times.  The number of internet users have helped in accelerating the pace. VARIETY: From pictures, videos to information and numbers, the type of data pouring in through various channels is varied as it can get VOLUME: The amount of data generated every minute is growing in leaps and bound. As per statistics, the average amount of data generated every minute is US alone is 2,657,700 gigabytes. As data has been multiplied hundreds of times, its analysis has become even more difficult. Data comes in all kinds of forms – structured and unstructured.  To make this data useful by drawing information and insights, we need systems which are much more advanced than traditional databases. This is where Big Data tools and analytics come in. “Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” – (Geoffrey Moore, author and consultant) In this article, we will walk you through some of the best big data tools to look out for in 2019 TOP BIG DATA TOOL YOU MUST KNOW HADOOP: If there’s a discussion about big data, it is incomplete without a mention of Hadoop. Literally, Hadoop and big data are inseparable. Even in 2019, Hadoop remains significant and relevant in the world of Big Data analytics. SO, WHAT IS HADOOP? Hadoop is an open-source framework that helps in storing data and tackling big data in an efficient way. It first came into existence in early 2000s; around the same time as Google.  It began as a search engine indexing tool and grew more technical with features like storing and processing data. Over time, hadoop has become synonymous with big data analytics and still remains important. It is low cost and has easy accessibility. It has four components – Hadoop Common:  basic utilities for every kind of case. Hadoop Distributed File System (HDFS): a database for storing data in a simple manner Hadoop MapReduce:  Helps in processing and simplifying a large set of data by filtering and analysis. Hadoop YARN: helps in resource management and scheduling. Even though, it is as old as the term “Big Data”, it still remains the backbone of it. Thanks to its affordability, open libraries and scalability, Hadoop still has a growing scope in 2019. Learning Hadoop will give you a solid base and understanding of Big Data. It will also help you in learning other technologies like Apache Spark etc. APACHE SPARK: Developed in 2009 at UC Berkeley, Apache Spark is one of the most popular open-source data processing engines with APIs in varied forms like Java, Python, SQL and R. Apache Spark was developed to provide speed, ease of use and sophisticated analysis. In an article in Forbes, Apache Spark was called Taylor Swift of Big data as it has been around for a while but grabbed eyeballs only around 2015. In the past few years, Apache Spark has gained a lot of admirers mainly because of the following reasons – It uses electronic memory rather than completely relying on hard disk which makes it 10 times faster. Even though it doesn’t have its own database, it can easily be integrated with any system like HDFS, MongoDB and Amazon’s S3 system. It is the most preferred framework for Machine Learning. Machine Learning is the present and the future of technology. With the help of Spark Streaming, data can be analyzed in real-time. Start Learning Apache Spark Apache Spark was developed as an improvisation of Hadoop and is still flourishing. It is now considered as one of the key and mature tools for Big Data Analytics. Even with introductions of new technology, Apache Spark continuous to rule the Big Data ecosystem. APACHE CASSANDRA: The software world runs on scalability. And, Apache Cassandra is a highly scalable, no SQL, open-source framework. Open-sourced in 2008 by Facebook, Apache Cassandra provides certain advantages that no other relational database or SQL can give.  Some of these advantages are – – DECENTRALIZATION: Apache Cassandra doesn’t have a master-slave architecture. Every cluster is identical eliminating a single point of failure or network bottlenecks. – HIGHLY ELASTIC: Its unique decentralized architecture makes adding new nodes easy, thus, enables it to handle a large amount of data across channels. – LINEAR SCALABILITY: Scaling is easier and simpler as no single node is interdependent on each other, and adding a new node can help you scale as much as you would like. Apache Cassandra has been helping big shots like Apple, Spotify, Instagram, ebay etc. Its ability to handle multiple concurrent users without affecting the performance makes it the first choice of many organizations. In 2019, Apache Cassandra will only continue to grow as more and more people realize its benefits. MongoDB: Just like Apache Cassandra, Mongo DB is another NoSQL database. With its high flexibility, cost-effectiveness and open-source libraries, Mongo DB is the fastest growing technology. It is simple, dynamic and object-oriented. What makes it different from the traditional databases is its document store model in which data is stored as a document rather than in columns of a traditional database. Some of the advantages it offers are – Because of its rich document-based data system in the form of BSON etc, a large variety of data like integer, string, array can easily be stored. – Its infrastructure is cloud-based making it highly flexible. – It uses dynamic schemas which allows data to be set up quickly. This helps in saving cost and time – It helps in the real-time analysis of data. Mongo DB is highly preferred for e-commerce websites, social networking sites and content management.  All of these are the need of the hour in 2019. As the most important part of MEAN Stack, it is the most preferred framework by startups. Moreover, bigger companies too are adopting it quickly. APACHE SAMOA : Coming back to the Apache family, Apache SAMOA is one of the most popular big data tools especially for the graphical databases.   SAMOA stands for Scalable Advanced Massive Online Analysis and really, the abbreviation explains it all. Apache SAMOA is an open-source platform which has a collection of the distributed streaming algorithm for data mining and machine learning tasks such as – -Regression, -Clustering -Classification -Program Abstraction It has a pluggable architecture which allows it to run on many distributed stream processing engines like – – Apache Storm – Apache S4 – Apache Flink Apache Samoa is the most preferred framework for Machine learning as it facilitates the development of new machine learning algorithms without interfering with the complexity of underlying distributed stream processing. Some major reasons why it is preferred are – With its pluggable and reusable architecture, deployment becomes easy. – There’s no downtime. – Once a program is completed, it can be run everywhere. – Simple process. CONCLUSION: Big Data is the biggest trend of 2019 and of the coming future. An understanding and proficiency in big data help in carving a bankable career path for yourself. Our suggestion would be to begin your journey in Big Data with Hadoop and Spark. These two technologies are a pioneer in the world of Big Data and will always remain the basis of analytics. For more information, feel free to contact us and check our training program.

Big Data Hadoop Tutorial for Beginners

Introduction to Big Data Big data refer to all the data generated through various platforms across the world. A data is classified as big if the total size is more than 1 GB/TB/PB/EX. Categories of BigData: 1) Structured 2) Unstructured 3) Semi-structured Example of BigData: 1) New York Exchange generates about 1 TB of new trade data per day. 2) Social Media:  Statistics show that 500+ terabytes of data get ingested into the database of social media site Facebook, every day. Data mainly generated in terms of Photos & video uploads Message exchanges Putting comments etc. 3) Jet Engine /Travel Portals: Single Jet Engine generates 10+ terabytes(TB) of data in 30 minutes of a flights per day.  Generation of data reaches up to many Petabytes (PB). What is Hadoop? Hadoop is an open-source framework managed by The Apache Software Foundation. Open source implies that it is freely available and its source code can be changed as per the requirement. Apache Hadoop is designed to store & process big data efficiently. Hadoop is used for data storing, processing, analyzing, accessing, governance, operations & security. Large organization with a huge amount of data uses Hadoop software, processed with the help of a large cluster of commodity hardware. Cluster term refers to a group of systems that are connected via LAN and multiple nodes on this cluster helps in performing the jobs. Hadoop has gained popularity worldwide in managing big data and at present, it has covered nearly 90% market of big data. Suggested Read:- DIFFERENCE BETWEEN DATA SCIENCE, DATA ANALYTICS AND MACHINE LEARNING Features of Hadoop: Cost-Effective: Hadoop system is very cost-effective as it does not require any specialized hardware and thus requires low investment. The use of simple hardware known as commodity hardware is sufficient for the system. Supports Large Cluster of Nodes: A Hadoop structure can be made of thousands of nodes making a large cluster. Large cluster helps in expanding the storage system & offers more computing power. Parallel Processing of Data: Hadoop system supports parallel processing of the data across all nodes in the cluster, and thus it reduces the storage & processing time. Distribution of Data(Distributed Processing): Hadoop efficiently distributes the data across all the nodes in a cluster. Moreover, it replicates the data over the entire cluster in order to retrieve the data from other nodes, if a particular node is busy or fails to operate. Automatic Failover Management (Fault Tolerance): An important feature of Hadoop is that it automatically resolves the problem in case a node in the cluster fails. The framework itself replaces the failed system with another system along with configuring the replicated settings & data on the new machine. Support Heterogeneous Cluster: the Heterogeneous cluster is one that accounts for nodes or machines which are from a different vendor, different operating system and running at different versions. For instance, if a Hadoop cluster has three systems, one IBM machine that runs on RHEL Linux, the second is INTEL machine running on Ubuntu Linux and third is AMD machine running on FEDORA Linux. All of these different systems are capable to run simultaneously on a single cluster. Scalability: A Hadoop system has the ability to add or remove node/nodes and hardware components from a cluster, without affecting the operations of the cluster. This refers to scalability, which is one of the important features of the Hadoop system. Must Read:- WHY BIG DATA WITH PYTHON IS TOP TECH JOB SKILL  Overview of Hadoop Ecosystem: The Hadoop ecosystem consists of 1) HDFS (Hadoop Distributed File System) 2) Apache MapReduce 3) Apache PIG 4) Apache HBase 5) Apache Hive 6) Apache Sqoop 7) Apache Flume, and 8) Apache Zookeeper 9) Apache Kafka 10) Apache OOZIE These components of the Hadoop ecosystem are explained as follows: HDFS (Hadoop Distributed File System): HDFS has the most important job to perform in the Hadoop framework. It distributes the data and stores them on each node present in a cluster simultaneously. This process reduces the total time to store data onto the disk. MapReduce (Read/Write Large Datasets into/from Hadoop using MR) : Hadoop MapReduce another most important part of the system that processes the huge volume of data stored in a cluster. It allows parallel processing of all the data stored by HDFS. Moreover, it resolves the issue of the high cost of processing through the massive scalability in a cluster. Apache PIG (PIG is a kind of ETL for Hadoop Ecosystem): It is the high-level scripting language to write the data analysis programs for huge data sets in the Hadoop cluster. Pig enables the developers to generate query execution routines for the analysis of large data sets. The scripting language is known is Pig Latin which one key part of Pig & the second key part is a Apache HBase (OLTP/NoSQL) sources: It is a column-oriented database that supports the working of HDFS on real-time basis. It is enabled to process large database tables i.e. a file containing millions of rows & columns. An important use of HBase is the efficient use of master nodes for managing region servers. Apache Hive (HIVE is SQL Engine on Hadoop): It is a language similar to SQL, which allows the squaring of data from HDFS. The Hive version of SQL language is called as HiveQL. Apache Sqoop(Data Import/Export from RDBMS(SQL sources) into Hadoop): It is an application that helps in import & export of data from Hadoop to other relational database management systems. It can transfer the bulk of data. Sqoop is based on connector architecture that backs the plugins for establishing connectivity to new external systems. Apache Flume(Data Import from Unstructured(Social Media sites)/Structured into  Hadoop)  : It is an application it allows the storage of streaming data into the Hadoop cluster, such as data being written to log files is a good example of streaming data. Apache Zookeeper ( Co-ordination tool used in a Clustered environment (Hadoop)): Its role is to manage the coordination between the above-mentioned applications for their efficient functioning in the Hadoop ecosystem. Functioning of Hadoop – HDFS Daemons Hadoop system works on the principle of master-slave architecture. HDFS daemons consist of the following: Name Node: It is the master node, and is single in the It is responsible for storing HDFS Metadata that keeps track of all the files that are stored in the HDFS.  The information stored on Metadata is like the file name, file permission it has, authorized user of the file & the location where it is stored. This information is stored on RAM which is generally called as file system Metadata. Data Nodes: It is the slave node, and is present in multiple numbers. Data nodes are responsible for storing & retrieving the data as instructed by the name node. Data nodes intermittently report to the name node with their present status & all the files stored with them. The data nodes keep multiple copies of each file stored in them. Secondary Name Node: Secondary name node is present to support the primary name node in storing the Metadata. On the failure of name node due to corrupt Metadata or any other reason secondary name nodes prevent the dysfunctioning of the complete cluster. The secondary name node instructs the name node to create & send fsimage & editlog file, upon which the compacted fsimage file is created by the secondary name node. This compacted file is then transferred back to name node and it is renamed. This process repeats after every 1 hour or when the size of editlog file exceeds 64MB. The functioning Hadoop system can be better understood with the help of a live example. Let us take the example of a banking system. Banks are required to analyze loads of unstructured information with them which is collected through various sources such as social media profiles, calls, complaint logs, emails, discussion forums, and also through traditional sources of collecting information like cash and equity, transactional data, trade, and lending, etc. for better understanding & analyzing the customers. The financial firms are now adopting the Hadoop system in order to structurally store the data, access the data, and analyzing & extracting the key information from the data that will provide comprehensive insights to help to make the right & informed decision. Join Gyansetu's Big Data Hadoop Training in Gurgaon for the best career. 

Best 10 uses of MS Excel in Daily life

In the quick & progressing lifestyle of people, there is utmost demand for shortcuts & advanced methods to understand and resolve the daily problems. Moreover, there are multiple tasks use of Excel that we do in our daily lives such as calculating monthly expenses, budgeting & goal setting, students tacking their syllabus and various other things that most people do in casual ways rather than following simple tools that provide concrete shapes. Here are the Best 10 uses of MS Excel in our daily lives Use of Excel for Students & Teachers:    Teachers can make the best use of table styles, charts, shapes, data tools, and various formulas to educate students in the classrooms. Whereas students can enhance their learning skills to solve basic and logical statistical & mathematical problems in excel. Use of Excel for Goal Setting & Planning: Goal setting & planning are the kinds of repeated tasks carried on each day. From business owners to students to housekeepers each individual is involved with the process of goal setting and planning. The goal-setting and planning process involves white paper, time, and the immense pressure of calculations, but with the use of MS Excel, this process has become efficient, quick, easy, and environmentally friendly. Must Read:- 5 REASONS WHY MICROSOFT EXCEL IS IMPORTANT FOR ANALYTICAL CAREER Use of excel for Entrepreneurs & Business Owners:   A large chunk of the millennial population across the world is aspiring to become an entrepreneur which requires not only efficient planning but also analysis of team performance, work progress, business progress & payout detail. Whether a new business or an established business, each can derive benefits from using excel. The data can be stored, analyzed and sophisticated presented on excel sheets using multiple tables, pivot tables, data highlighters, sorters, sheet and cells organizers among others. Use of Excel for Housewives: Housewives are known to be the best keepers of monthly expenses & saving money. And thus excel can assist housewives to manage their daily house expenses which can track down the spending habits of each member of the house. Furthermore, housewives using Excel can be beneficial for them to make their kids learn basic excel skills. Must Read:- TOP 5 MOST IN DEMAND IT JOB SKILLS YOU NEED FOR THE FUTURE  Use of Excel for Career Development: Career Development revolves around career management. The tasks such as learning management, time management, work, and life management and goal-focused habits are important which can be effectively practiced on MS excel. Use of excel for Monthly Expenses Report: Based on monthly expense data entered in the excel sheet, the user can create a comprehensive monthly expenses report which can highlight the top expenditure segments and study the pattern of expenditure & required savings pattern to be followed to reach the desired goal. Start Learning Advanced Excel VBA  Use of excel for Online Access: Another important use of MS Excel is that the files can be accessed online from any part of the world anytime and anywhere. It provides the convenience of accessing the excel files over mobile phones even if there is the unavailability of laptops & lets you exercise the work easily without any problem. Use of excel for Developing Future Strategies:  The data relating to future strategies such as investments and major expenses anticipated at future date can be entered in the form of charts and graphs so it can lead to identify the trends and compare each possibility. Using MS Excel, the trend lines can be expressed through graphs and charts to forecast the future value of money invested. Use of Excel for Create a Calendar or Schedule:  Whether it is family-based planning for a weekly, monthly or yearly calendar or a personal appointment daily planner or a schedule for managing bill payments, homework, favorite sports team’s games, and many more, excel can make it easy to compile, filter, search, organize and simplify large amounts of data. Use of Excel for Event & Project Planning:  MS Excel is being widely used in the planning of a large work project or holiday or wedding party, where it can keep track of different tasks, efforts, and deadlines, and to analyze the schedules of collaborators in the planning of the event with other participants and make use of excel sheet as a central database of all relevant information required by all members to execute the project or event. The more a person gets deeper in learning the wide benefits of excel, the more they tend to develop smarter ways to apply MS excel in daily life. It is better to provide some time on learning & getting hands-on excel rather than doing things in unnecessary and less productive ways. Other than the above-mentioned benefits, various other uses of excel can be developed as per the individual requirements.   

How to Get a Job as a Data Scientist?

The world runs on data. And, only an expert data scientist can make this humongous amount of data useful. As the human dependency on the internet and data grows, so grows the demand and popularity of data scientists. The statistics, also, proves that data science is one of the most sought-after job profile in 2019. Let us give you some figures to help you understand our point – At present, there are over 97,000 job openings for data science in India in 2019. As per LinkedIn, data science job openings witnessed an increase of over 56% in the past year. It has been touted as the “Best Job in America” for the past 3 years. It has also been named as “The Most Promising Job in America” in 2019.   Even after these huge claims, there are qualified data scientists who are jobless. A quick talk with any of these aspirants would lead you to the core of the problem. Data Science is a huge field that requires a spectrum of skills. Your proficiency in one skill but a lack of another could cost you your career. Then, how do you ensure that you find a job you are looking for? Or, how do you attain the right skills for the profile? What are the ways to reach the right recruiter at the right time? In this article, we explore these dilemmas. Hopefully, by the end of it, you will be heading towards your dream data science job. So, grab a mug of coffee and hop on; it’s going to be a long ride. WHAT EXACTLY ARE YOU IF YOU’RE A DATA SCIENTIST? You are the excel whiz kid at your office. You can mine through complex data and find the right information. Are you a data scientist? You are a pro at Python. You can figure out Machine Learning Algorithms easily. Are you a data scientist? The answer to these questions is yes and also, no. As mentioned before, data science is a huge field which encompasses many skills, concepts and techniques. Most students and fresh graduates do not know how to match their skills with the right profile. If you apply for a position which doesn’t require your skillset, you’re bound to get rejected. The first step towards getting the right job is to know what the right job is for you. Let us give you a quick look at different data science profiles, their required skills and their responsibilities for you. DATA  ANALYST: In very simple words, a data analyst scrutinizes raw data to find out crucial information. This information helps an organization make decisions, plan strategies and achieve goals. A data analyst is required everywhere – in banking, software development companies, telecommunication and even manufacturing industry You must have 1) good mathematical and statistical aptitude 2) Knowledge of languages like Python and SQL ) Advanced Excel skills – both micro and macro. DATA  ARCHITECT: A data architect helps in creating and maintaining database models for an organization to achieve strategic goals relating to data management. They help in collating, retrieving and maintaining company’s data and information. Thy also analyze structural requirement for new software and applications. You need to have a knowledge of a) RDMS including MS SQL or NoSQL and Cloud computing depending on the organization, b) Various Hadoop technologies like Hadoop, Spark, MapReduce etc c) understand data mining models, d)knowledge of languages like Python and R  DATABASE  ADMINISTRATOR: A database administrator ensures that the right data is available to all the stakeholders whenever required. They also ensure the proper function and security of various databases of an organization. From maintaining backups to introducing new data technologies, a database administrator does it all. In order to work on this role, you need to have a) an understanding of Oracle, Microsoft SQL Server, MySQL, or cloud computing b) Knowledge of database design c) Understanding of distributed computing architecture  BUSINESS  ANALYST: One of the most important non-technical role in the world of data science. A business analyst is the bridge between business side and the technical nerds of a company. They use sophisticated data science insights to help make an important business decision You need to a) understand various data models to make better predictions b) have a knowledge of data visualization tools like Tableau c) have commendable communication and convincing skills.  DATA  MANAGER: He/she’s the captain of the ship. They combine their technical skills and experience with project management to execute effective data science strategies in an organization. For this, you need every technical skill and a lot of experience. Of course, these are not the only data science job profiles available. You could also be a statistician, a database developer or a data engineer. Understand the difference between data science, data analytics and Machine Learning If you understand the requirement of a particular profile, you would know which profile is the right fit for your skills and qualification. If you plan to work in a particular profile, you can start with obtaining the skills required to perform that job right. An understanding of business requirements helps you in modifying your skills and aptitude for a better career. When you know what is expected from you, you perform better. I APPLY TO THE RIGHT PROFILE BUT I NEVER GET A CALLBACK . How you convey is much more important than what you convey. A recruiter gets more than 100 resumes for a particular profile. You need to ensure that your resume makes to the final lot. Find out the important keywords related to data science and include them in your resume. Nowadays, there’s software that screens the resumes to shortlist the relevant ones (All hail AI). Make your resume algorithm ready. Try to fit the most important information about you in the first page. To be frank, restrict your resume to one page – nobody looks past that. In the real world, your experience matters more than your degree. Include your projects, assignment, and relevant job experience, if any, before education Make yourself visible online. Join LinkedIn and get inside its data science groups. Start posting your knowledge and about your experiences in data science on various online forums. It’s a world of the web and only your network can help you reach places. Start working on it today. I HAVE THE RIGHT CERTIFICATIONS AND TRAINING, YET, I CAN’T FIND A JOB? Thanks to our education system, we equate the number of degrees with the abilities of a person. Sadly, the professional world doesn’t work this way. Along with the required qualification, you also need enough experience or at least, practical knowledge to prove your worth. Fresh graduates get easily dejected as they believe that no work experience dims their prospects. While this is true, there is number of ways that can ensure that your profile looks inviting. We let you in a few secrets to get the profile few brownie points. Here they are Spend your time applying the concepts you learn in your training class. Take up as many live projects and assignments as possible. Publish your work on GitHub. This will also act as your portfolio and help you prove your competencies before the interviewer. Take part in Kaggle competitions and do not forget to include them in your discussions. Make a report of every project you undertake which describes the entire process. Make algorithms your best friend. When a recruiter looks for a data science professional, they want someone who understands the working of data. Moreover, you must take up a course in a place where more emphasis is given to the practical application of concepts. You can take up free online courses available on Udemy, Upgrad and other such platforms. You could also join an institute where you could learn through classroom training. Whatever you choose to be your mode of learning, please do keep in mind the following pointers. Choose a place which has experienced trainers. Your queries must be given top priority. The quicker you solve them, the better your understanding. Take a look at the kind of live project opportunities they provide. An active placement cell can help your cause greatly. Make sure the syllabus is updated as per the new developments. Speak to the alumni. They will give you a better picture. Selecting the right teachers sets the foundation for your future. They lead you into the industry. Speaking of training, Check Gyansetu's Data Science Training Course.  Our Suggestion to Become A Data Scientist:-  When you have the right skills, you will eventually end up at the right place. However, in the middle of lakhs of job seekers, you must take a few steps to compel recruiters into hiring you. Finally, we would like to offer you some final tips to ensure that you become the next star of the data science world – Learn Python and R. Brush up your knowledge about Big Data tools. Maintain a record of your projects. Establish online visibility. Listen to the recruiters and follow their advice. They want you hired (their incentive depends on it). Lastly, get your training from the right place. (Preferably, someone like us) Data Science always needs some tech nerds. If you follow our advice, you will be soon one of them.    

Corporate Clients