Contact Us
×

Category: Analytics

What is R Programming For Data Science

Posted on by Imarticus Learning

Data sciences have become a crucial part of everyday jobs. The availability of data, an 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.

Why R is Best Suited to Data Analytics:

The Foundation for R opines that R is an environment-creating language for graphics and statistical computing. Originally developed by Robert Gentleman and Ross Ihaka at New Zealand’s Auckland University sometime in the early 90s, the free-to-use, open-source statistical framework platform R has evolved and has been used in thousands of libraries created and used by various data analysts.
• R is an object-oriented language used for data analysis by data-scientists, analysts, and statisticians for predictive modeling, statistical analysis, and data visualization.
• R is also a language used for programming since it provides for functions, operators, objects, etc that allows statisticians to make sense of, explore, visualize and make models from statistical data.
• R is the ideal statistical analysis environment due to the ease of implementing statistical methods. It is very popular for research applications and its ability for predictive modeling allows techniques to be vetted before implementation in R.
• R is open-source and hence free to use requiring no license or extra software to run it. Its quality has evolved due to its popular use, open interfaces, and numerical accuracy that allows it’s being used compatibly with most systems and applications.
• R has a large user community. Its leadership includes global computer scientists and statisticians who also have a forum for 2 million plus users who are constantly helping evolve it into a well-supported language with an extremely well-supported community.

How It Compares With Python:

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 processes of 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 beautifulsoup 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 a 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 modeling analysis with Numpy, scientific computing with SciPy and the sci-kit learn code library with machine learning algorithms are some excellent working features in Python.
The R’s core functionality and specific modeling analyses 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.

In parting, before choosing to learn just one language, ask yourself why you want to do a course in R for data science? Is it for programming experience, research, and teaching, working in the industry, studying statistical or ML in data sciences, visualizing data in graphics or just interest in software engineering?

Research Data science Training well and you will find that depending on what functions you need both are excellent languages to learn for a career in data sciences. At Imarticus Learning, R is used widely to understand data analytics and then move to learn Python for data analytics.

Is Python Required for Data Science? How Long Does It Take to Learn Python for Data Science?

Posted on by Imarticus Learning

Data Science and its analytics require good knowledge and the flexibility to work with statistical data including various graphics. Python is tomorrow’s language and has a vast array of tools and libraries. Its installation program Anaconda works with many operating systems and protocols like XML, HTML, JSON, etc. It scores because it is an OO language well-suited for web development, gaming, ML and its algorithms, Big Data operations, and so much more.
Its Scipy module is excellent for computing, engineering and mathematical tasks allowing analysis, modeling, and even recording/ editing sessions in IPython which has an interactive shell supporting visualization and parallel computing of data. The decorators of functionality are a good feature in Python. Its latest V3.6 features the asyncio module, API stability, JIT compiler, Pyjion, and CPython aids.

Uses of Python:

Learn-by-doing for tasks involving python for data science and Big data Analytics will help in the following.
Web development can be easy with Flask, Bottle, Django, Pyramid, etc especially to cover even the backend REST APIs.
Game development is enhanced through Pygame where you can use the module to create a video game.
Computer VisionTools like Opency, Face detection, Color detection, etc is available in Python.
Scraping the web from websites that cannot expose data due to lack of an API is regularly done by price-comparison e-commerce sites, news and data aggregators using Python libraries like BeautifulSoup, Requests, Scrapy, PyMongo or Pydoop.
Tasks involving ML algorithms like identification of fingerprints, predicting stock prices, spam detection etc using ML is supported by Python’s modules like Theano, Scikit-learn, Tensorflow, etc. Even Deep Learning is possible with Tensorflow.
Developing cross-platform GUI desktop application is a breeze with the Python modules of PyQt, Tkinter etc.
Made-easy Robotics uses Raspberry Pi as its core which can be easily coded on Python.
Data Analysis from both offline/online data needing cleaning can be achieved in Pandas. Matplotlib can help find patterns and data visualization which are essential steps before applying any ML algorithm.
Browser Automation tasks like browser opening, FB posts and status are quick using Python’s Selenium.
Content Management tasks including advanced ones are relatively faster with Plone, Django, CMS etc.
Big Data libraries are more flexible and use as a learning tool.

How to Learn Python:

Here is a step-by-step approach to becoming a Kaggler on Python from an absolute Python newbie complete with tools and ready to kick-start your career in data-sciences.
Step 1: Read, learn and understand why you are using Python:
Zero in on your reasons for learning to use Python, its features and why it scores in data sciences.
Step 2: Machine set-up procedures:
Firstly use Continuum.io to download Anaconda. Just in case you need help, refer to complete instructions for the OS by just clicking on the link.
Step 3: Python language fundamentals learning:
It is always better to gain experience from a reputed institute like Imarticus Learning for doing a course on data analytics and data sciences. Their curriculum is excellent and includes hands-on practice, mentoring and enhancing practical skills in Python.
Step 4: Use Python in interactive coding and Regular Expressions:
When using data from various sources the data will need cleaning before the analytics stage. Try assignments like choosing baby-names and data wrangling steps to become adept at this task.
Step 5: Gain proficiency in Python libraries like Matplotlib, NumPy, Pandas and SciPy.
Practice in these frequently used libraries is very important. Try out these following tasks and resources like NumPy tutorial and NumPy arrays, SciPy tutorials, Matplotlib tutorial, the ipython notebook, Pandas, Data munging and exploratory analysis of data.
Step 5: Use Python for Visualization:
A good resource is linked in the CS 109 lecture series.
Step 7: Imbibe ML and Scikit-learn:
These are very important data analysis steps.
Step 8: Use Python and keep practicing:
Try hackathons like Kaggle, DataHack and many others.
Step 9: Neural networks and Deep Learning
Try out short courses on the above topics to enhance your skills.
In conclusion, many reputed institutes offer a Data science Course. The course at Imarticus also offers other advantages such as learning through convenient modes and timings, global updated industry-relevant curriculum, extensive hands-on practice and certification that ensure you use the mentorship to be career and job-ready from the very first day.

What are some really interesting Machine Learning projects for beginners?

Posted on by Imarticus Learning

 

We are witnessing an era of the data revolution. Every organization across the world are trying to make use of data to improve their business. As a result, the demand for skilled Data scientists is skyrocketing. We know that Machine Learning is an important part of Data Science and the Best way to learn it is, of course, practicing it. Any professional taking a Machine learning course should be doing their own projects. Practicing your lessons will help you get familiar with the common ML libraries. Here are a few projects you could try along with your machine learning training.

1. Iris Flowers Classification ML Project

It is the “Hello world” of Machine Learning. This project involves classifying the flowers into 3 different species according to the size of their petals. You can use the Iris Flowersdataset which consists of the numeric attributes of each flower. This data set is considered to be the best available in this classification genre. You will have to use Supervised Machine Learning algorithms to load and handle this data. Also, you can work on this small data without any special transformation or scaling.

2. BigMart Sales Prediction ML Project

The product of this project is a regression model that can predict the sales in 10 BigMart outlets spread across 10 different cities. You can use the BigMart Dataset which consists of the sales data of 1559 products from 10 different outlets. Using Unsupervised Machine Learning algorithms, you can predict the sales of each 1559 product in each outlet.

3. Analysis of Social Media Sentiment Using Twitter Dataset

There are huge amounts of data created by our social media platforms on a regular basis. By mining these data we can understand a lot about the trends, public opinions, and sentiments going on the world. Among them, the data created by Twitter is fund to be best suited for beginners. Using the Twitter data set which consists of around 3 MB data, you can find out what is world talking about the various topics such as movies, elections or sports. This project will help you develop skills in social media mining and classifiers.

Data Science Course

 

4. Recommender system with Movielens Dataset

The modern customers are looking for more customized content everywhere. The applications like Netflix and Hulu are using recommender systems to find content matching each of their customers. This project is about making such a recommender system. You can use the Movielens Dataset which contains around 1.000,200 movie ratings of 3,900 movies made by 6,040 users. You can start building this recommender system with a World-cloud visualization of the movie titles.

5. Stock Prices Predictor

If you would like to work in the finance domain, this project is an excellent choice for you. The aim of this project is to build a predictor system which can learn about the performance of a company and forecast its stock price. You will have to deal with a large variety of data such as prices, volatility indices, fundamental indices and many more. The dataset required for this project can be found at  Quandl.

These projects will introduce you to some challenges and their solutions in machine learning. Your machine learning certification will be complete only with such a hands-on experience with ML.

 

What are some Data Analytics Internship questions?

Posted on by Imarticus Learning

 

What do Data Analysts do?

Data analytics (DA) is the science that deals with examining raw data sets to understand the useful information they contain. This process is aided by specialized software systems. Data analysts use technologies to facilitate organizations to take business decisions in a more efficient way. The main goal is to boost the business performance of the company by improving operational efficiency and increasing the profit rates.  The positions of a business analyst, data analyst and a data scientist differ in terms of technicality. In other words, the business analysts are least technical, data analysts being more technical and the data scientists are the most technical.      

Scope and Career Prospects of Data Analytics

The scope of data analytics is progressively huge in India. Every workplace being more technology-based, there is a great demand for professionally trained data analysts, who can efficiently record and analyze data to solve business problems.  

Data analysts can work in companies that offer banking services, fraud detection jobs, telecommunications, etc. Also, they can find employment in any private technology firms and in big reputed tech companies. In India Bengaluru, hosts 27% of analytics jobs, followed by Delhi and Mumbai.

Now that you are aware of the scope of data analytics, you should join data analytics courses that offer certification and alumni.

Data Analytics Course

Why Take Up Data Analytics As a Career?

  1. Bachelor’s degree is not enough, because a specialized degree is important.
  2. The increasing demand for data analysts in today’s world. 
  3. Data analytics can be a worthwhile contribution to your profession.
  4. It is a rewarding career, you can get a higher income. 

So, take up data analytics as a career and get a great opportunity to work with renowned Multi-National Companies.   

Qualifications of Data Analyst Intern

  1. Problem-solving skills
  2. Good communication skills and analytical skills.
  3. Strong business awareness.
  4. Knowledge in SQL.
  5. Programming knowledge and application skills.
  6. Efficient in Excel.
  7. Bachelor’s degree.

7 Data Analyst internship interview questions

What is the responsibility of a data analyst?

The responsibilities of a data analyst are,

  • To resolve business-related issues for clients.
  • To analyze results and interpret data by using statistical techniques.
  • To identify new areas for improvement.
  • Filter and clean data
  • Review computer reports. 

What are the steps involved in an analytics project?

The steps involved in an analytics project are:

  • Problem defining.
  • Data exploring.
  • Data preparation.
  • Validation of data.
  • Implementation.

What is data cleansing?

Data cleaning is the process of identifying and removing errors from data in order to improve the quality of data.

What are the best tools useful for data analysis?

  • Tableau
  • RapidMiner
  • OpenRefine
  • KNIME
  • Google search operators
  • Solver
  • Wolfram Alpha’s

What can be done with suspected data or missing data?

  • A validation report should be prepared, which gives all the information about the suspected data.
  • Examine the suspicious data to determine their acceptability.
  • To work on missing data, use the best analysis strategy like deletion method, single imputation methods, etc.
  • Invalid data should be replaced with a validation code.

Explain N-gram?

An n-gram is a contiguous sequence of n items from a sequence of text or speech. It is a type of probabilistic language for predicting next item.

What is Map Reduce?

It is a framework to process large data sets, splitting them into subsets and processing each subset on a different server and blending results.

How do you build a career in Machine Learning after completing the ML Foundation Course?

Posted on by Imarticus Learning

 

ML/Machine Learning has a promising future. Chatbots, smartphones and most AI platforms essentially use ML. For example, Alexa from Amazon, Google, Facebook, and almost all large platforms point to a growing industry and an all-time high ML jobs demand. Very obviously the need for professionals in ML, AI, and Deep Learning outstrips the demand.

Programmers, graduates in Computer Applications, and even graduates in mathematics, Social Science or Economics can learn and become ML professionals by doing a certified foundation course in Data Analytics/ Data Science course.

The ML professionals essential skill set include

·         Computer programming and CS Fundamentals.

·         Programming languages like R, Python and some more.

·         ML libraries and algorithms.

·         Statistics and Probability.

·         Software design and systems engineering.

Simple ways to get started with Machine Learning:

A. Read ML books and do a machine learning course with a reputed company like Imarticus which can provide you with reinforcement and certification of your practical skills. Data is the beginning and all about applying your machine learning training, programming knowledge, computer science techniques and statistics to data. R and Python are the most commonly preferred languages. While Python scores in leveraging libraries that are analytics-friendly, practical algorithms, the application development and end-to-end integration using sci-learn and Tensorflow APIs, R is preferred for advanced capabilities in data and statistical inferences analysis.

B. Hone your ML skills with ML Courses which provide ML fundamentals and basic algorithms, statistical pattern recognition and data mining. Your knowledge of statistics should include Bayesian probability, inferential and descriptive statistics for which you will find free courses by Udacity.

C. Applying your learning to building algorithms like perception and control for robotics, building smart robots, anti-spam, and web-search text understanding, medical informatics, computer vision, database mining, and audio based applications.

D. Attend hackathons (Kaggle, TechGig, Hackerearth, etc) which give you support, exposure and mentorship in  ML practical ideas.

E. Build your portfolio with 

  1. A project where you collect the data yourself 
  2. A project where you deal with data cleaning, missing data, etc

F. Master areas that you like to work in like Neural Networks, AI, and ML as applied to image segmentation, speech recognition, object recognition and VR.

The Job Scope:

ML can be the most satisfying choice of careers today which include algorithm development and research used for adaptive systems, building predictive methods for product demand and suggestions, and exploring extractable patterns in Big Data.  Companies recruit for positions like 

  • ML Analyst 
  • ML Engineer 
  • Data Scientist NLP 
  • Lead- Data Sciences 
  • ML Scientist.

Expected payouts:

According to a Gartner report, 2.3 million ML jobs in AI are expected by 2020. Entering the ML field now, according to Digital Vidya, is a great option because the ML payouts for the new entrants vary from Rs 699,807- 891,326. With good expertise in algorithms and data analysis the range of reported salaries could be from Rs 9 lakh to Rs 1.8 crore pa.

How The Travel Industry Uses Big Data and Real-Time Analytics?

Posted on by Imarticus Learning

In this article, let us see how big data has brought about tremendous changes to the tourism sector, allowing the mushrooming of successful unicorns lie OYO, Trip Advisor, RedBus and many more to flourish. Big data and real-time analytics have helped the tourism industry rediscover itself and reap huge rewards.
Better decisions, improved customer experience, foresight on marketing campaigns, competitors, etc. have allowed strategic funding and decision making to boost tourism revenues. Did you know that OYO with a total corpus of 185 million dollars and a pan India presence in 223 cities used big data and real-time analytics to enable check-ins which total over two million?
So what exactly is real-time analytics in Big-Data about? 
Using data is normal, and we continue to generate it using everyday devices like smartphones. What was once in terabytes is today huge volumes of petabytes! That is big-data, and it comes from a number of sources, as text and video messages, blogs, posts, etc. in social media and internal company data. The essence of big data and real-time analytics is to clean and scour this data using deep learning techniques to enable self-learning of intelligent algorithms and networking with neural networks between databases to give gainful insights into trends, behavior patterns and the probability of occurrences. Such foresight is accurate, evidence-based and useful across a variety of functions like finance, behavior analysis, accounting, budgeting, marketing, customer services, and daily operations at all!

The top 5 Ways in which the travel industry is impacted:

1. Managing revenues:

Being able to sell the right product, through the right media channel, at the right price, at the right moment and to the right customer is the crux of excellent financial management and increasing profits. In the travel industry hotel bookings, vehicle management, local events, flights, holiday seasons, occupancy rates, room prices, prior reservations, availability of rooms, and many such factors affect revenues and its management. Which of us has not heard of Trivago, Expedia and such apps?
2. Building brands:
Reputation management has become necessary with the increasing use of social media, internet, reviews, posts, and blogs being referred to and used in making decisions like flight, destination and hotel bookings. Customer satisfaction and quick resolutions of issues is another critical area for increasing brand loyalty exhibited through reviews and posts. Smart pricing, discounts, seasonal fares and such are most often based on real-time analytics, feedback, surveys, and customer user experiences. Thus building brand loyalty has become a concerted effort at training and using data analytics effectively. Just look at how Google searches, Facebook, etc. always suggest your favorite sites when making a booking or purchase.

3. Promotional and marketing strategy:

Finding the right group of customers to target, deciding on promotional campaigns, the method, timing, and media, budgeting and execution of marketing plans are effectively a result of smart use of databases, trend spotting, foresight, and predictive analysis. Thus marketing messages pop up based on customer interest, time, location, etc. to save big when you make a booking on Yatra, Goibibo, etc.

4. Enhancing the experience of customers:

Customers are hard to please and ensuring their loyalty is based on improving the customer experience. Be it the hotel bookings, flight experience, Forex transactions abroad, or finding the best price big data analytics can help make those apps more effective for both the firm and customer. Modern times has even seen Airtel international travel cards, new-age banking UPI apps, QR scanning on PayTM, cashless transfers on PayPal, and shared Uber or Ola cabs in an effort to deliver improved customer experience based on insights from big data and real-time analytics.

5. Effective use of analytics and market research:

Today, data has evolved to be more precious than any other asset, especially in the tourism and travel sector. Market research using real-time big-data analytical techniques provides the basis of operations and all its allied functions today. Just ask to Make my Trip, Treebo Hotels, Bespoke Hotels or RedDoorz.
Conclusion:
Big data and its analytics can be beneficial to the travel industry through a number of applications that produce outcomes and foresight that are enablers of decisions that are actionable. Such apps have been a boon in online booking, optimizing dynamic prices, predicting demand, targeting markets, enabling strategy for financial budgeting and marketing plans. Improving the customer experience has led to higher sales, and the travel industry today is a booming sector offering a plethora of jobs and opportunities.
Do the Big Data Course at the reputed Imarticus Learning to get a firm grasp of how to be an enabler of the travel industry. The scope for growth and payouts are high. Don’t let the opportunity slip by you.

What is the practical advice for Machine Learning?

Posted on by Imarticus Learning

Read, and re-read resources on introductions to Calculus, Mathematical statistics, both differential and inference, algorithm analysis, optimization, differential equations, linear algebra, Python, R and more. Does that sound difficult?

You don’t need advanced learning in them. You will however essentially need to understand how you can apply this learning to handling data analysis of the present and future of nearly every field under the ML, AI, Deep Learning and VR fields.

Here are some advantages of machine learning training in such courses.

  • You get to learn ML fundamentals and basic algorithms, statistical pattern recognition, data mining, statistics including Bayesian probability, working with Python, Pandas, and R, the Sci-learn and Tensorflow APIs, and more in a well-paced, learn-at-your-convenience online and classroom training mode.
  • The integrated curriculum helps you through practical industry-needed and relevant practical applications like

1. Unsupervised learning (deep learning, clustering, recommender systems, dimensionality reduction)

2. Supervised learning (neural networks, support vector machines, parametric/non-parametric algorithms, kernels)

3. ML best techniques and practices (variance and bias theory, AI, and innovation in the ML process).

  • Most learning is through applications, case studies, live-industry-project and effective mentoring, virtual classes, workshops, hackathons, and such support.
  • Your certification carries weight as it declares you have applicative knowledge and our job and industry ready.

Data Science Course

That having been said, here are some practical tips for ML and discerning learners.

  • The first timers in ML rarely get things right. Don’t panic. ML skills are cultivated skills and are meant to be regularly practiced.
  • Implement your learning through a model. Compare your implementation skills with others while discovering the open-source libraries, mathematical or program techniques, and tricks, math-tools, etc. that can improve your efficiency.
  • Don’t get overwhelmed because leveraging your skills means research-work and doing small projects which help assimilate learning and applying the learning to practical situations whether they be smartphones, VR or chatbots. The tools in Python take care of the math while you get your hands deep into data analysis, data cleaning, and mining and data exploration and predictive analysis.
  • It isn’t just about math for beginners. Most often it is about data, data and more data! So get cracking in honing your data analysis skills.
  • Apply your learning to building algorithms like perception and control for robotics, building smart robots, anti-spam, and web-search text understanding, medical informatics, computer vision, database mining, and audio based applications.
  •  Attend hackathons (Kaggle, TechGig, Hackerearth, etc.) which give you support, exposure and mentorship in  ML practical ideas.
  •  Build your portfolio with projects

a. Where you collect the data yourself

b. Where you get exposure to data cleaning, dealing with missing data, etc.

  • Master areas that you like to work in like Neural Networks, AI, and ML as applied to image segmentation, speech recognition, object recognition and VR.

As in all fields, it does get easier as you progress and get adept. So why wait? Partner with Imarticus courses and get a head-start in ML. Go ahead and do a machine learning course with a reputed training institute like Imarticus.

Karen’s Review of Imarticus Learning’s Post Graduate course in Big Data Analytics

Posted on by Imarticus Learning

Karen Soares, a student of Imarticus’ Post Graduate course in Big Data Analytics shares her journey from an IT graduate to an Analytics professional and a job at Peel Works.
Tell us a little about yourself.
My name is Karen Soares. I was born and brought up in Mumbai, and I graduated in B.Sc. IT, also in Mumbai. I joined Imarticus for the postgraduate program in Data Analytics. I currently work at Peel Works as a Data Analyst owing to the efforts of Imarticus Learning’s placement team.
Tell us about your experience with Imarticus.
My experience with Imarticus was really good; it was much better than I expected. I came across Imarticus by taking an online assessment on their website, and the next day I received a call from a counselor asking me to turn up for a counseling session. Initially, I was apprehensive and did not want to attend the counseling; however, the counselor persuaded me to come, and I am thankful for that. Once I arrived at Imarticus’ Mumbai office, I had an insightful session with the counselor who helped me pick the right course based on my academic and professional goals.
What has changed since you joined Imarticus Learning?
Since I joined Imarticus my life has changed drastically. I feel that I am much more confident in myself and my professional abilities. I have a complete understanding of what I do, what I work for and what I work as and that makes a lot of difference. I realized that what we learn in school and college is a bit sketchy and has barely any practical applications. But what I’ve learned at Imarticus through the practical learning approach has really stuck with me. I have a fantastic job because of Imarticus, and I enjoy going to work every day.
What do you like most about Imarticus?
The thing I like most about Imarticus is the level of comfort and approachability that they provide. Every professor here is always ready to solve your doubts and is prepared to answer all your questions – a hundred times if needed. You can never be afraid to ask seemingly silly questions, and that makes the learning experience much better. Everybody at Imarticus was accommodating throughout the course, any questions and queries were always answered at the earliest, and that’s what makes an excellent institute.
Are you on the right track to achieve your Analytics aspirations? Click Here and speak to a career counselor today!

How can you start programming machine learning and artificial intelligence?

Posted on by Imarticus Learning

One of the biggest developments in the world of computing over the last few years has undoubtedly been artificial intelligence. The ability for a machine to automatically learn and apply methods to improve the quality of output is one of the most in-demand jobs in the world today. Companies are willing to pay big bucks to create systems that can understand their user and make predictive techniques based on their behavior.
These techniques and systems are already being employed by some of the biggest companies in the world. If you’re looking to start your machine learning course, here are a few basics you need to know:
R or Python:
Python and R are two of the most commonly used programming languages with algorithms in all fields dependent on them. While Python is used more in the field of machine learning, it is also easy to understand and learn. Organizations have already implemented it in places to develop applications on analytics. It makes it easier for users to implement any type of algorithms as well.
R is used to create better and more statistical processes. R is generally used to create and formulate statistical processes and companies dependent on data analytics tend to use R.
Statistics:
A basic understanding of Statistics is necessary to comprehend machine learning. While you might need to know what the algorithm does, knowing how the tools can be used for the end result is also necessary. With time, you’ll be able to implement your own algorithms as well and create inferential and descriptive statistical methods at some point.

Artificial Intelligence Course
What skills would you require?
If you’re looking to get a job in the field of artificial intelligence or machine learning, there are a few essential skills, including:

  1. Communication skills – An ability to communicate is crucial in addition to professionally having a good knowledge of spoken English
  2. Education – A good graduation degree or A.I. certificate is also required to begin a career in the field of education. This is needed to create your base in this particular field.
  3. Programming languages – A knowledge of programming languages – understanding of python string, variables, statement, operators, conditions, modules, and sense are super necessary.
  4. Machine learning techniques – Knowing all about Artificial Intelligence, especially Machine Learning as it is the most lucrative field. It has helped to create powerful websites, given realistic speech popularity and more
  5. TensorFlow – This is a software program that is periodical for the dataflow to be streamlined to execute different duties. It is generally used for gaining practice in the systems, including the relation to nerve networks.
  6. Deep Learning – Knowing about deep learning is necessary to use strategies that are rooted in the evaluation of learning statistics, in order to create a unique set of rules and processes.

Hence, with the time you will be able to understand everything about the field of machine learning and artificial intelligence. With Imarticus, you can get the machine learning certification and begin your journey right away!

Sandeep’s Review of Imarticus’ Data Science Course

Posted on by Imarticus Learning

We caught up with Sandeep, a recent graduate of the Post Graduate program in Analytics, for a quick chat to get his perspective on the program, the curriculum, Imarticus Learning’s placement process and more.
Tell us a little bit about yourself.

Sandeep: My name is Sandeep Singh. I recently completed my B.Sc. in Computer Science and was looking for an avenue to enhance my analytics skills and start my career.

Data Science Course in MumbaiI came across Imarticus’ data science course and, after thorough research, decided to enroll for it. I completed the course and have been placed at M Technologies through Imarticus.

How has your experience been with Imarticus Learning?
Sandeep: My experience with Imarticus Learning was super! The course focused on practical training with hands-on learning of various analytical tools and thorough practice with numerous datasets.

Looking back, I see the importance of actually applying Analytical tools and techniques to the projects I worked on because it gave me a running start when I began working.

What has changed since you joined Imarticus Learning?
Sandeep: Since the day I joined Imarticus my confidence has been boosted to a very high level. Through the practice of various analytical tools such as R, Python, SAS, Tableau, etc. I’ve come to believe in myself. My soft skills have also been elevated with the help of business communication workshops, mock interviews, and soft skill sessions throughout the course.

Would you recommend the program to someone else?
Sandeep: While researching various institutes, I came across some reviews that say Imarticus Learning is fake. Well, I wanted to see for myself and now that I have, I would definitely recommend Imarticus. If you’re looking for an institute, the first thing that comes to mind is the faculty and the learning material.

The faculty and staff are very cooperative and help you both inside and outside the classroom. The learning material is extensive and covers every aspect of data analytics. The best part is all of the lectures, notes, datasets, and quizzes are stored in an online Learning Management system and is available to students anytime, anywhere.

What do you like most about Imarticus?
The best thing about Imarticus Learning was the course content, the cooperative staff and the informative notes that are easily accessible. The resume building workshops and mock interviews definitely prepared me for the placement drives and I was able to crack the interview and land a job at M Technologies.

Looking to get started on your data science career, Speak with a counselor and get matched with the best course for you.

Get a free 1:1 Imarticus counselling
Get a free 1:1 Imarticus counselling