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Tag: course on data analytics

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.

7 Skills That Data Scientists Need To Know Via Big Data Analytics Courses

Posted on by Imarticus Learning

Data analytics is one of the most sought-after careers of today. Being a good data scientist involves developing a lot of skills essential to the job.
Here are a few skills you need to have on your resume if you want to become a good data scientist:

1.  Being capable of handling unstructured data

Unstructured data refers to any data that cannot be made to fit into any database tables. This data can include customer reviews, audio clips, blogs, posts, or even videos. Arranging such data into specific categories can be quite the daunting task. As a data scientist, you must be able to work with a lot of unstructured data. Some software that you need to know how to use for this purpose are NoSQL, Microsoft HDI insight, Polybase, Apache Hadoop, Presto etc.

2.  Good knowledge of Mathematics and Statistics

A good understanding of statistics is essential for anyone looking to become a data scientist. You must be familiar with all kinds of statistical concepts such as distributions and tests. Also, making predictions requires that you are familiar with the basic operation of calculus and linear algebra.

3.  Using data to tell a story

It is always easier for clients to understand data analytics if it is presented in a visual format using graphs, charts etc. Therefore you must have the capability to visualize raw data in a form that the layman can understand.

4.  Programming Skills

As a data scientist, you will be working with a lot of software that will require you to enter the code manually. As such, you must have a good knowledge of programming languages such as R and Python, which are normally used in data analytics. You must be able to write, understand and correct any code no matter the circumstances.

5.  A Competitive Spirit

As a data scientist, you will have to work on your toes more often than not. Therefore, it is essential for you to have a competitive spirit that will help you thrive. Hackerearth and NMIMS are two of the platforms that conduct hackathons, seminars and other competitions where you can gain more knowledge and understand all the latest trends in data analytics.

6.  Working on Projects

You must take up some live projects so that you get some hands-on experience in the field. This is important since most companies are looking for data scientists who are experienced in the field.

7.  Academic Qualifications

Most companies prefer their data scientists to have done their master’s degrees in the fields of computer sciences, mathematics, statistics and physical science. If you’re interested in working with research companies, then it will be advantageous to have a PhD in the same subjects.

Should You Start With Big Data Training or Learn Data Analytics? Which One to Start First?

Posted on by Imarticus Learning

 
It is always a better choice to learn Big data training rather than generalize with data-analytics which is a very large field. Today’s world deals with not just Big Data but the term for big have increased by many multiples of big in terms of data volume. Further, the tools that are used are fast evolving and learning the Big-Data tools first can be done online and through courses. Once you have proficiency in dealing with big data you can also do data analytics courses and understand better the concepts of analytics while applying them to databases classified as big and very, very big!

Difference Between Data Analytics And Big Data

The languages and tools used and the end purpose is different in the two courses one being used in managing large database sets while the other focuses on gaining and providing insights from such datasets. Data science covers courses to learn how to visualize data, make predictive models using R/Python and then use manipulation techniques on the data to get foresight and forecasts or trends. Big Data courses are about managing the data systems and databases. Tools used in Big data training are Hadoop, Tableau, R, NoSQL, and many others that deal with managing the data and integrating the results to give the desired dashboards, visualizations, graphics and summary of statistics.
The R language is taught in data sciences and includes R as its programming language because of its tool range to deal with statistical and analytical applications. The applications used need R programming and hence R developers would be more preferred. Big data training on the other hand, uses MapReduce for Java-based installation programs, needs to integrate and connect with R through Tableau from the Hadoop library and uses data processing tools like Flume, Hive, Sqoop, HBase etc
Learning Hadoop Course
You can use online resources and do it yourself using top10online courses.com. However, formal training has many advantages and is highly recommended. Join the Big data training Hadoop course at a reputed institute like Imarticus Learning. Hadoop has a vast array of subsystems which are hard to learn for the beginner without formal training. The course helps you assimilate the ecosystem and apply these systems to solving real-world industry-related problems in real-time through assignments, quizzes, practical classes and of course do some small projects to show off your newly acquired skills. The best part is that you have certified trainers leading convenient modes and batches to help you along even if you are already working.
Start building your project portfolio and get on GitHub.
The steps that follow are the Hadoop progressive tutorial in brief.
• Hadoop for desktop installation using the Ambari UI and HortonWorks.
• Choose a cluster to manage with MapReduce and HDFS.
• Use Spark, Pig etc to write simple data analysis programs.
• Work on querying your database with programs like Hive, Sqoop, Presto, MySQL, Cassandra, HBase, MongoDB, and Phoenix.
• Work the ecosystem of Hadoop for designing applications that are industry-relevant.
• Use to manage your cluster with Hue, Mesos, Oozie, YARN, Zookeeper, and Zeppelin.
• Practice data streaming with real-time applications in Storm, Kafka, Spark, Flume, and Flink.

Why do a data analytics course?

Today it would be exceptional if a company does not use Hadoop and data analytics in one form or the other. Among the ones that you can easily recollect are New York Times, Amazon, Facebook, eBay, Google, IBM, LinkedIn, Spotify, Yahoo!, Twitter and many more. Big Data, Data Analytics and Deep Learning are widely applied to build neural networks in almost all data-intensive industries.
However, not all are blessed with being able to learn, update knowledge and be practically adept with the Big data training Hadoop platform which requires a comprehensive ML knowledge, AI deep learning, data handling, statistical modelling and visualization techniques among other skills. One can do separate modules or certificate Big-Data Hadoop training courses with Imarticus Learning who offer such learning as short-term courses, MOOCs, online classrooms, regular classrooms, and even one-on-one courses. Choices are aplenty with materials, tutorials and options for training being readily available thanks to high-speed data and visualization made possible by the internet.
Doing a formal data analytics training course with certification from a reputed institute like Imarticus Learning helps because
• Their certifications are widely recognized and accepted by employers.
• They provide comprehensive learning experiences including the latest best practices, an updated curriculum and the latest training platforms.
• Employers use the credential to measure your practical skills attained and assess you are job-prepared.
• It’s a feather in your hat that adds to your resume and opens doors to the new career.
• Knowledge in Analytics is best imbibed through hands-on practice in real-world situations and rote knowledge gained of concepts may not be entirely useful.
The best Big data training courses for Advanced Analytics are available at the IIMs at Lucknow, Calcutta, and Bangalore or at the IITs of Delhi and Bombay. This is an apt course for people with lower experience levels since their curriculum covers a gamut of relevant topics in depth with sufficient time to enable you to assimilate the concepts.
The courses run by software training institutes like Imarticus are also excellent programs which cost more but focus on training you with the latest software and inculcating practical expertise. Very experienced professionals are likely to get corporate sponsorship and can avail training at competitive discounted rates. Face-to-face lab sessions, mandatory project work, use of role-plays, interactive tutoring and access to the best resources are also very advantageous to you when making the switch.
Job scope and salary offered:
Persons with up to 4 years experience can expect salaries in the range of 10-12 lakhs pa at the MNCs according to the Analytics India Magazine. Yes, the demand for jobs in this sector will never die down and is presently facing an acute shortage.
Conclusion:
In parting, there are plenty of options that you can research more on. It is worth it when your Big data training certification helps you land the dream career you want immaterial of the route you followed. Whether you prefer managing databases and then getting at the insights or choose to get the insights and then learn how to train and manage the datasets is your choice. Both choices will be in demand for jobs over the next decade. So don’t wait. Take that leap into data today!