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Tag: data science courses in India

Does Machine Learning Excite You? Check Out Our Data Analytics Course!

Posted on by Imarticus Learning

Machine learning (ML) is truly a blessing to modern computing and technology, possessing the ability to endow systems and machines, the ability to think for themselves and tackle tasks on their own without any supervision of humans. Machine learning is able to do this by creating artificial neural networks which simulate how human brains work. Machine learning is assisted by data science and supports its applications in various fields.

Even though machine learning was initially invested upon with the primary focus on Artificial Intelligence, it was later recognized as a separate field and started being heavily invested upon from the 1990s and is one of the most valuable fields of computing that has one of the highest industry requirements of skilled professionals and freshers holding expertise in various skills and tools which assist in machine learning.

In this article, we will learn more about machine learning and how a well-planned data analytics course can help you progress in your career if you are already in this field or how it can help freshers get exposed to ML. 

What is machine learning?

Machine learning first came into existence due to the interest of having systems and computers learn from data on their own. “Machine learning” was first termed by Arthur Samuel in 1959, who was working in IBM at that time. During his tenure there, he was responsible for various important projects related to computer gaming and AI. It all started when Mr. Samuel took the initiative to teach computers how to play games through the game of Checkers on IBM’s first commercially available computer, the IBM 701.

Eventually, machine learning started being used for various purposes and borrowed many models and approaches from statistics and probability theory. AI uses predictive analytics along with machine learning to execute the various responses or trigger actions. All of this is acquired from the training data set which helps the machine in learning and equips it with the information.

Machine learning is an important branch of computing and data science that creates autonomous systems which learn from data on their own. A machine trained with clean processed data eventually identifies trends and patterns to respond to situations without human supervision.

Machine learning also promotes the automatic improvement and development of algorithms or data models which improve on their own. Machine learning is an important part of Artificial Intelligence which uses data mining, predictive analytics, and various tools to assist machines in learning more extensively with methods like deep learning to allow them to execute functions that emulate the responses of a human, just much more accurate and fast.

Machine learning is also not biased unless specifically asked to do so, hence promoting unbiased AI-supported systems that make fewer errors. Data mining is also a very relevant field and quite valuable to machine learning as it helps systems come to conclusions without having some bits of data or having unknown bits of information. Machine learning is a type of predictive analytics which is backed by data and is exploratory in nature.

Perks of a Data Science Prodegree from Imarticus

The Data Science Prodegree is a great data science course that students and working professionals can choose to gain more exposure and skills in the fields of machine learning, business analytics, and AI.

 

  • Acquire skills and learn how to use required tools and algorithms
  • Gain valuable industry and course certifications
  • Get placement support and opportunities from the best companies
  • Advanced live classroom learning supported by technology and real-life projects

 

Imarticus’s Data Science course with Placement is a great choice if you wish to advance in your career and learn about machine learning, AI, business analytics, or data analysis which will help you become more effective as a data scientist and pursue your dream career in this respectable field.

The Impact of Data Science on Current Events and the World

Posted on by Imarticus Learning

The Impact of Data Science on Current Events and the World

Data science remains one of the most lucrative and challenging career pathways for experts. Successful data professionals now grasp the traditional skills of analyzing massive quantities of data, data mining, and programming.

best data science courses in IndiaData scientists must control the complete spectrum of the data science life cycle and must be flexible and understandable so as to optimize returns at each stage of the process to detect meaningful intelligence for their organizations.

You can also contribute to this surge by doing proper data science online training.

Skills that data scientists must have:

According to a study by IBM, a data scientist must be able to perform the following tasks:

  • Use math, statistics, and a scientific approach
  • Use a variety of tools and strategies for data assessment and preparation – for example, SQL, data mining, and data integration methods
  • Data extraction through predictive analysis and artificial intelligence (AI), including in-depth learning and models
  • Write apps for data processing and calculating automation
  • Tell — and illustrate — stories that show the importance of findings at every level of technical knowledge and comprehension to decision-makers and stakeholders
  • Explain the use of these results for business challenges

The number of job opportunities in the industry is increasing by more than 5% a year, according to an IBM study.

What is the role of data science in the current scenario?

  • Inadequacies can cost companies up to 30% of their income. The data science course allows you to follow a number of business indicators, including manufacturing times, delivery expenses, productivity for employees, and more, and suggest improvements.

It is feasible to reduce total expenses and increase return on investment by limiting waste of resources.

  • Data science enables companies to consistently refine their products and services to suit a changing market by assuring a ready-flow of practical insight into customer psychology, behavior, and satisfaction.

Data on clients can be accessed from a range of sources, and information mining from third-party platforms such as social media, search engines, and data sets.

  • One of the most intriguing aspects of data science is testing. New, inventive options are compared with current features and often produce surprising outcomes.

Companies can create incremental revenue gains through consistent, long-term testing. Data scientists are in charge of conducting thorough tests to ensure the effectiveness of marketing campaigns, product launches, job satisfaction, website optimization, et al.

  • Data science is used in the current scenario to improve a company’s safeguarding of sensitive information. Banks, for example, deploy sophisticated machine-learning algorithms for detecting fraud based on variations from a user’s normal financial activities. Because of the vast volume of data created every day, these algorithms can detect fraud faster and more accurately than humans.

Algorithms can be utilized to protect sensitive information via encryption.  By ensuring data privacy you can help guarantee that your organization does not misuse or reveal sensitive information about its consumers, such as credit card numbers, medical information, or Social Security numbers.

  • Data collection and analysis on a bigger scale can help you spot developing trends in your market. Purchase information, stars and influencers, and search engine searches can all be utilized to discover the things people want.

Conclusion

It can be concluded that a career as a data scientist is an extremely lucrative option in the current world as data science is gradually taking over the entire world. The data science pro degree can help you understand the intricacies of this field and learn data science effectively.

If you are a recent graduate and want to learn data science, a post-graduate program in data analytics and machine learning can help you learn better from live faculty and bag guaranteed jobs in the future. Proper data science online training can help the audience come here.

Become A Data Scientist And Start Your Career With A-list Firms !

Posted on by Imarticus Learning

Become A Data Scientist And Start Your Career With A-list Firms !

If you’re wondering why data science courses have become so popular recently, then it’s because the demand for data scientists is very high among A-list firms.

In the following points, we’ll discuss some of the biggest companies that hire data scientists and how you can start a career in one of them by taking an online data science course in India

Top companies that hire data scientists

Microsoft

If you’re using a PC, then you’ve probably heard of Microsoft already. Microsoft is a software development company and powers millions of computers throughout the world through its Windows operating system. The average pay of a data scientist at Microsoft is $136,000 per year.

Uber

Uber is an online cab-hailing service that has become widely popular due to its innovative solution. It uses data science to improve its operations, optimize route selection for drivers, calculate better fares, and a plethora of other tasks. The average pay of a data scientist at Uber is $139,000 per annum.

Pinterest

Pinterest is a social media platform for sharing and finding images. Social media platforms require the expertise of data scientists to help them enhance their algorithms and offer a better customer experience to the users. The average pay of a data scientist at Pinterest is $212,000 per year.

Google

Google is probably the most popular tech company in the world. It is also among the best employers for data scientists. The average pay of a data scientist at Google is $138,000 per year. Being a simple search engine, Google has expanded into a large tech enterprise with various subsections.

How to become a data scientist

As you can see, becoming a data scientist can help you bag lucrative jobs in some of the world’s top companies. Starting a career in data science is quite easy as well.

You will need to join a data science course in India to learn the necessary skills for this role. Online data science courses have become increasingly popular among students and professionals alike who want to start a career in this field.

You should look for data science courses in India that teach you the latest in-demand skills such as R, statistics, predictive analysis, Hadoop, and Spark. It would be best to get a data science course with placement support. When you join a data science course with placement support, you get the opportunity to start your career right after completing the online data science course in India.

Be sure to check the relevant data science course details (such as data science course fees and eligibility requirements) while looking for data science courses in India.

Conclusion

The companies we talked about aren’t the only ones that hire data scientists. Data science professionals work in numerous sectors as well, including finance, education, healthcare, and manufacturing.

Online Data Science Courses in India

Now, you know that you can start a career in this field by taking a data science course in India, you should head to our site where you’ll find more data science course details, including the data science course fees, requirements, and curriculum.

Take That Next Step Towards a Rewarding Data Science & Analytics Career With These Analytics Courses

Posted on by Imarticus Learning

Take That Next Step Towards a Rewarding Data Science & Analytics Career With These Analytics Courses

Are you interested in completing a data science course in India but don’t know where to start? Then you’ve come to the right place as we’ll discuss the top data science courses in India and learn how they help you start a career in this fast-growing industry.

All of the courses below have reasonable data science course fees and you can choose according to your requirements and aspirations.

Data Science and Analytics CareerWe’ll discuss the data science course details of our programs in the following points:

Post Graduate Program in Analytics and Artificial Intelligence

Our post-graduate program in analytics and artificial intelligence is among the most popular data science courses in India. We offer this program with UCLA Extension. It is a data science course with placement assurance which means you will get access to our dedicated placement support to our private placement portal and additional services.

The program gives you a dual certification from UCLA Extension and Imarticus Learning. UCLA Extension is one of the oldest and largest higher education providers in the United States. Some of the key concepts you’ll study in this online data science course in India are Machine Learning Algorithms, Deep Learning, Computer Vision, and many more.

Machine Learning and Deep Learning Prodegree

Machine learning refers to the field of developing computer solutions that can perform tasks and learn from them without requiring human intervention. Our Machine Learning and Deep Learning Prodegree will help you learn the required skills to enter this field as a skilled professional.

We offer this program with IBM. The course teaches you machine learning, Python, IBM Watson, and deep learning through 16 in-class and industry projects with a Capstone project as well.

Post Graduate Program in Data Analytics

Our Post Graduate Program in Data Analytics teaches you data science from scratch. It is among the best data science courses for beginners as it covers all the required concepts.

You will learn the foundations of data science and its in-demand tools including Python, R, PowerBI, Tableau, Hadoop, SQL, and Spark. Like our other programs, it is a data science course with placement support to help you start your career right away.

Data Science Prodegree

We offer our data science prodegree with KPMG. The program is industry-aligned and teaches you the most in-demand skills in the industry. You will work on real business case studies and receive project mentorship directly from industry experts.

This online data science course in India teaches you SQL, programming, Tableau, statistics, R, Python, and many other important concepts. You will also work on a KPPG in India Capstone Project by the end of this data science course in India.

Conclusion

Starting a career in data science and analytics is quite simple. All you need is a little effort, commitment, and guidance and the rest is easy.

Now that you’re aware of our data science course details, you can start your learning journey right away. You can find out more information on our data science course fees and eligibility criteria on our website.

Why It Is Right Time To Pursue A Career in AI, ML and Data Science?

Posted on by Imarticus Learning

Introduction

The world is all set for a digital transformation. New technologies are disrupting how business is being conducted on a day-to-day basis. Among the most notable of these technologies are Artificial Intelligence (AI), Machine Learning (ML), and Data Science.

These technologies are constantly restructuring the landscape of different economies throughout the globe, as it provides tremendous career opportunities. Moreover, these technologies are also interrelated which gives an individual a chance to build a holistic, well-paying, and satisfying data science career.

Career In Data ScienceWhy now is the Right Time?

We are living in the age of the fourth industrial revolution where everything is expected to be data-driven. Moreover, the pace at which the volume of data is growing is simply astonishing.

According to an IBM survey, 90 % of the data available has been created in the last two years. Technological devices like smartphones, tablets, and laptops have revolutionized the way users interact with the internet, and this number of users is also increasing at an exponential speed.

Now, accumulating data is not enough. An analysis of data is required to produce insights that can help in the curation of actionable results. This is exactly where the tools of AI, ML, and Data Science become relevant. These tools leverage various techniques from mathematics, statistical modeling, data engineering, data visualization, computer programming, cloud computing, etc.

To extract the insights from data collected by an organization. Now, this insight forms the basis of strategic decision-making in any organization. It is used to create targeted ads, augment customer experiences on company websites, reduce costs, forecasting demands, and so on. Therefore, the application of predictive algorithms like AI, ML, and data sciences are pervasive throughout different functional domains.

Again, these tools are used across different organizations as well. Governments, Corporates, Brands all are leveraging the advancements in technology to create an entire automated, data-driven ecosystem. Therefore, naturally, there has been an upsurge in the demand for data science courses in India and data science jobs across industries and functions. It is estimated that in India close to half a lakh positions have opened up.

Data Science CareerFrom an Indian context only, a typical data scientist is expected to receive a salary of around INR 9 lakhs p.a. Similarly the salary figures for AI and ML engineers would lie at around INR 5.5 lakhs p.a. and INR 11 lakhs p.a. respectively. Therefore, a six-salary figure makes a career in these disruptive technologies even more attractive.

With the pandemic changing the operation models across industries and functions, it can be safely assumed that technology is going to become even more relevant. Data Science, AI, and ML have a steep learning curve more and more organizations are adopting newer and agile techniques.

From expensive platforms, SPSS, SAS, etc. and organizations are now moving to open resource platforms like python and R. Therefore, technology is no more the future anymore; it is here and those who are passionate about it can find a lucrative career opportunity in AI, ML and Data Science.

Do Data Scientist Use Statistics?

Posted on by Imarticus Learning

Do Data Scientists Use Statistics?

Data science has been the buzzword of the tech industry for the past few years. Everyone is aware of the endless opportunities and large pay scale awaiting the data scientists. But when the question becomes “what do they do?” or “how do they do it? ” Only a few people know it. This article discusses whether data scientists use statistics in their operations. Read on to find out.

Statistics in Data Science
Statistics can be a very powerful tool in data science. It is simply the use of mathematics to analyse the data technically. The following are the few important instances where data scientists use statistics.

  1. Design Experiments to Inform Product Decisions.
    Data scientists use Frequentist Statistics and experimental design to determine whether or not the difference in the performance of two types of products are significant to take action. This application help data scientists to understand the experimental results especially when there are multiple metrics being measured.
  2. Models to Predict the Signal
    Using Regression, Classification, Time series analysis and casual analysis, data scientists can tell the reason behind a change of rate of sales. They use these techniques to predict the sales of upcoming months and point out the relevant trends to be careful of.
  3. Turning Big Data Into Big Picture
    Consider a large group of customers buying products. The data about each person’s shopping list is worthless if it stays like that. Data scientists can label each customer and put similar ones into a group and understand the buying pattern. It helps to identify how each group of people affect the business development. Statistic techniques such as clustering, latent variable analysis and dimensionality reduction are used to achieve this.
  4. Understand User Engagement, Retention, Conversion and Leads
    It is known that many customers would be lost from the signing-in stage to the actual regular use stage. Data science use techniques such as regression, latent variable analysis, casual effect analysis and survey design to find out the reason behind this loss. It also identifies the successful leads the company is using to engage more customers.
  5. Predicting the Customer Needs
    Statistical techniques such as latent variable analysis, predictive modelling, clustering and dimensionality reduction help data scientists to predict the items a customer might need next. A matrix of users and their interactions with the company product is all that is needed to obtain this.
  6. Telling the story with Data
    It is the end product of all operations of data scientists. He acts as the ambassador between the company and data. All the findings from data should be properly communicated with the rest of the company without losing any fidelity. Rather than summarizing the numbers, a data scientist has to explain why each number are significant. To do that properly, data visualisation techniques from statistics are used. Clearly, data scientists use statistics to solve various problems in their day to day life. If data science seems the right career choice for you, don’t wait for long. Imarticus  Learning is now providing course on data science prodegree. This Genpact data science course will equip you with all the necessary skills for a successful data science career.

Which is better for data analysis: R or Python or else?

Posted on by Imarticus Learning

 

Data sciences have become a crucial part of everyday jobs. The availability of data, advanced computing software, and a focus on decisions that are analytics-driven has made data sciences a booming field. Jobs abound in this field and hence large interest also exists on which languages to learn. 

R and Python are the most popular tools for data science work. Both are flexible, open source, and evolved just over a decade ago.R is used for statistical analysis while Python is a programming language that can be termed general-purpose. These are both in combination essential for data analysis where you are involved in working with large data sets, machine learning, and creating data visualization insights based on complexities involving data sciences.

The process of Data Science:
 Very simply put the course on data science involve the four subdivisions discussed below. Let’s compare the two for the following.

Data Collection:
Python is supportive of different data formats. You can use CSVs, JSON and SQL tables directly in your code. You can even find Python solutions when stuck on Google. Rvest, magrittr, and beautiful soup packages in Python resolve issues in parsing, web scraping, requests etc.

Data can be imported from CSV, Excel, text files etc. Minitab or SPSS file formats can be converted into R data frames. R is not as efficient in getting web information but handles data from common sources just as well.

Data Exploration:
One can hold large volumes of data, sort, display data and filter large amounts of data using Pandas without the lag of Excel. Data frames can be redefined and defined throughout a project. You can clean data and scan it before you clean up empirical sense data.

R is an ace at numerical and statistical analysis of large datasets. You can apply statistical tests, build probability distributions, and use standard ML and data mining techniques. Signal processing, optimization, basics of analytics, statistical processing, random number generation, and ML tasks are easy to perform from its rather limited libraries.

Data Modeling:
 Numerical modelling analysis with Numpy, scientific computing with SciPy and the scikit-learncode library with machine learning algorithms are some excellent working features in Python.

The R’s core functionality and specific modelling analysis are rather limited and compatible packages may have to be used.

Data Visualization:
 The Anaconda enabled IPython Notebook, the Matplotlib library, Plot.ly, Python API, nbconvert function and many more are great tools available in Python.

ggplot2, statistical analysis abilities, saving of files in various formats like jpg, pdf etc, the base graphics module and graphical displays make R the best tool for statistical analysis complexities.

Before choosing, ask these questions
• Do you have programming experience?
• Do you want to do a Python course for business analytics or a business analytics course?
• Do you want to go into research and teaching or work in the industry?
• Do you want to learn ML or statistical learning in data sciences?
• Do you want to do software engineering?
• Do you want to visualize data in graphics?

Research well and you will find that depending on what functions you need both are excellent languages to learn for a career in data science.

How Should You Prepare For Statistic Questions for Data Science Interviews

Posted on by Imarticus Learning

Data Science has been the buzz word of the IT field for the past few years. Courses like data science course from Imarticus will equip you with all the skills required for a data science job. However, to ace the interviews for data science jobs, you should be well versed with the basic components of statistics too. This article discusses one of the key element in Data Science, statistics and its relevant topics to brush up before a data science job interview.
Preparing for Data science interviews
As in many interviews, the statistics are also going to start with technical questions. Many interviewers try to test your knowledge and communication skills by pretending to have no idea about the basic concepts and asking you to explain them. So, it is important to learn how to convey complex concepts without using the assumed knowledge.
Following are the few important topics you could brush off before attending the interview.
1. Statistical features
They are probably the most used statistics concept in data science. When you are exploring a dataset, the first technique you apply will be this. It includes the following features.

  • Bias
  • Variance
  • Mean
  • Median
  • Percentile and many others.

These features provide a quick, informative view of the data and are important to be familiar with.
2. Probability Distribution
A probability distribution is a function that represents the probabilities of occurrence of all possible values in the experiment. Data science use statistical inferences to predict trends from the data, and statistical inferences use probability distribution of data. So it is important to have proper knowledge of probability functions to work effectively on the data science problems. The important probability distributions in the data science perspective are the following.

  • Uniform Distribution
  • Normal Distribution
  • Poisson Distribution

3. Dimensionality Reduction
It is the process of reducing the number of random variables under consideration by taking a set of principle variables. In Data Science, it is used to reduce the feature variables. It can result in huge savings on computer power.
The most commonly used statistical technique for dimensionality reduction is PCA or Principal component analysis.
4. Over and Under-Sampling
Over and Under Sampling are techniques used to solve the classification problems. It comes handy when one dataset is too large or small relative to the next. In real life data science problems, there will be large differences in the rarity of different classes of data. In such cases, it is this technique comes to your rescue.
5. Bayesian Statistics
Bayesian statistics is a special approach to applying probability to the statistical problems. It interprets probability as the confidence of an individual about the occurrence of some event to happen. Bayesian statistics take evidence to account.
These topics from statistics are very important for a Data Science job and make sure you learn more about them before your interview. You can also try various data science training in Mumbai to begin your career at right note. Genpact data science course from Imarticus is an excellent choice to learn more about data science. Check out and join the course immediately.

How AI Drives Innovation in Next Generation Cloud Business Intelligence?

Posted on by Imarticus Learning

Today, we have access to a huge amount of technology and other systems through the internet – Artificial Intelligent systems are one of those. AI is becoming a larger part of our lives with each passing day, and the chances are that AI systems would already have affected us in some way or the other.
AI, in essence, is a predictive technology. The main function of every AI system is to essentially make a prediction based on the amount of data and information that it analyses. Since it can sift through any large amount of data, it is thus a type of technology that improves our lives in a huge manner. Similarly, the role of business intelligence and business analytics has changed too – it is now something that deals with increasing amounts of predictive analysis rather than historical analysis, and is available to users as an interactive, easy-to-use tool.

Thought Spot

Thought Spot is one of the pioneers in the segment of Business Intelligence – the California based company can be credited for creating a Google-like search engine which can analyse large amounts of data quickly and completely so as to provide the user with some great insights into the data. Thought Spot’s Ad-hoc version of data analytics provides various amazing services, like extremely transparent calculations into how each insight was derived, accompanying of natural language narratives with the rendered charts and a guided, curated search experience which generates suggestions for the users based on the role, the data model and the search history of the person. Thought Spot and its data analytics model is truly something to watch out for, in the future.

Anticipatory Models

Companies like Thought Spot and other data-driven Business Intelligence organisations are considered to be the forerunners of the next, and perhaps the largest wave in Business Intelligence called the anticipatory intelligence. They aim to leverage the usage of AI in a number of scenarios, like anticipatory devices, conversations and contexts. In this first one, the aim is to automate something that a large number of users are trying to do in a small time period so that it happens quicker and better. In the second and third, natural language processing systems are used so as to predict what the users are going to say, and thus promote rapid communication.
If all of this fascinates you, you should definitely look at the business analytics training courses and the data science courses that Imarticus Learning has to offer.