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15 Reliable Sources to Master Data Science

Posted on by Imarticus Learning

15 Reliable Sources to Master Data Science

Data Science is growing at a rapid pace and businesses have been dynamically benefitting from this. A lot of Data Science Courses are available at the Imarticus Learning Data Science Training Center. No doubt, the insights and knowledge of data science have helped business emerge a winner with better knowledge and insights available at their fingertips. Have a look at these 15 important blog resources with the highest number of followers if you are willing to understand and learn data science. These blogs have rich data science resources and won’t let you miss you anything in the world of data science.

  1. Reddit – It’s an American social news aggregation, web content rating and discussion website for everyone who loves to share content and satisfy their curiosity. The registered members at Reddit can submit content such as text posts or direct links and get opinions on the same. It’s a hugely popular website where everyone can participate because it’s simple and easy.

Frequency — About 84 posts per week

Facebook Fans: 1,108,745

Twitter Followers: 511K

2. Google News – Comprehensive and most dynamic up-to-date news coverage, aggregated from all over the world by google news. It’s a popular medium throughout the world since Google has become a most reliable name everywhere. It’s a reliable source of Data Science information where everything related to it will be at your fingertips.

Frequency — About 21 posts per week

Facebook Fans: n/a

Twitter Followers: 214K

3. Data Science Central – Now this is a platform where every kind of information is available in one place. It wouldn’t be wrong if we say that it’s the industry’s online resource for big data practitioners. And it’s damn popular among the practitioners. From analytics to data integration to visualisation, data science centre provides a community experience.

Frequency — About 24 posts per week

Facebook Fans: 1,013

Twitter Followers: 100K

4. KDnuggets I Data Science, Business Analytics, Big Data and Data Mining – Now, if you are looking for the most interesting and updated blogs on day to day evolution of the Big Data, then this is the place to be. Here, one can find the most interesting stuff on analytics, big data, data science, data mining and machine learning, not necessarily in that order.

Frequency — About 34 posts per week

Facebook Fans: 21,860

Twitter Followers: 96K

  • Kaggle I Data Science News – No Free Hunch – A competitive platform where companies and researchers post data while statisticians and data miners compete with each other to produce the best models for predicting and describing the data. It’s a popular platform where professionals compete with each other to come up with the best ideas that they have.

Frequency — About one post per month

Facebook Fans: 35,137

Twitter Followers: 89.1K

    • Revolution Analytics – An exclusive blog dedicated to the news and information of interest to the members of the community, who are deeply interested in analytics and relation disciplines. The blog is updated every US workday, with contributions from various authors.

Frequency — About six posts per week

Facebook Fans: n/a

Twitter Followers: 25.9K

  •  Data Science for Social Good – This social good data science does the work of training data scientists to handle the problems that matter. It effectively trains the data scientists to work on data mining, machine learning and big data.

Frequency — About one post per month

Facebook Fans: n/a

Twitter Followers: 20.5K

  • Data Camp – You can learn to be a data scientist from the comfort of your home through your browser with Data Camp’s data science blog. It’s a comfortable way where total information is available in one place, and you can pick up the topics that you want to master.

Frequency — About seven post per month

Facebook Fans: 340,109

Twitter Followers: 16.2K

9. Codementor – This blog tells you about the latest trends in data science. Here you can read tutorials, posts and insights from top data science experts and developers. This will eventually help you gain knowledge from experienced experts.

Frequency -About one post per month

Facebook Fans: 12,587

Twitter Followers: 22,1K

10. Dataversity – Data Science News, Articles & Education – Here, learn about the latest business intelligence news and get a thorough business intelligence education. This blog is focused more on the business side and understanding it is necessary from the business point of view.

Frequency – About one post per week

Facebook Fans: 6,312

Twitter Followers: 17.4K

11. Data science @ Berkeley I Online Learning Blog – If you are interested in an online course called professional Master of Information and Data Science (MIDS) from UC Berkeley School of Information.

Frequency — About one post per month

Facebook Fans: 14,804

Twitter Followers: 10.2K

12. Data Plus Science – This blog helps people find real answers in data science, quickly and effectively. So it’s a swift means of knowledge generation.

Frequency — About two posts per month

Facebook Fans: 2,932

Twitter Followers: 25.1K

13. NYC Data Science Academy Blog – A one-stop destination for in-depth development tutorials and new technology announcements created by students, faculty and community contributors in the NYC DCA network.

Frequency — About five posts per week

Facebook Fans: 2,136

Twitter Followers: 17,1K

14. Data Science 101 – A blog on how to become a data scientist.

Frequency — About five posts per week

Facebook Fans: 15,925

Twitter Followers: 2,365

15. Data Science Dojo – It’s a revolutionary shift in data science learning. The course offers short-duration, in-person, hands-on training that will get the aspiring data scientists started with practical data science in just a week!

Frequency — About one post per month

Facebook Fans: 12,009

Twitter Followers: 4,664

The Data Science Resources will help you keep updated and gain new knowledge and insights in the ever-evolving field of data science. The data science course at the Data Science Learning Center – Imarticus Learning will ensure updated knowledge to candidates.

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.

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.

Become Data Scientist in 90 Days

Posted on by Imarticus Learning

Data science is similar to any other field of science. The scientists involved conducted their own research and based on the information available form hypothesis and theories. However, in the case of data science, these hypotheses are created based on the data made available to the concerned scientists. The primary factor which an individual must consider in order to become a data scientist within a span of 90 days is to understand and to have a knack for analyzing data.
A career in data science is a hot topic in the present market. Organizations all around the globe are relying on big data, and for that skilled data, a scientist is required. Analysis of collected data involves the visualization of the data which is then backed up by creating reports after identifying specific patterns. However, what sets Data Science apart from the more traditional business analysis is the use of complex algorithms. The advanced algorithms such as neural networks, machine learning algorithms, and regression algorithms are used to scan the available data in order to identify the meaning and the purpose of the numbers and codes.
To become a data scientist an individual must have adequate knowledge about the fundamentals and the framework of these algorithms. This can only be possible when the concerned individual has a tremendous foundation for mathematics and statistics. So if you are aspiring to be a data scientist, make sure to get the basics right by keeping track of your mathematics as well as statistic skills.
Another foremost fundamental of data science is to know and understand the purpose of this study. The sole objective of a data scientist is to answer various questions. The study of data is carried out so that the probable questions can be answered by going through and analyzing a large set of recorded data. Let us consider the example of the popular entertainment network Netflix. In 2017, Netflix put forth a petition where a million dollars would be paid to a data scientist who would successfully improve the suggestion algorithm of the network.
Such is the demand and the requirement of the data scientist in the current market. Now for beginners, it is essential not to get into complex codes and a large amount of data. Analysis of large data would automatically mean the use of multiple algorithms. In order to become an efficient data scientist within a span of 90 days, it is critical to know personal strengths and weaknesses. Taking small steps helps as it builds confidence as well as enhances skill gradually. By considering these subtle factors, an individual can learn data science in no time and become proficient at it.
Another essential factor of becoming a data scientist is to go beyond the learning of Hadoop. There are many data science courses which not only helps you to be efficient with Hadoop but also assists you to gain real knowledge about reading and understand the various algorithms which are part of this data science game.
So to conclude, data science is a field which requires knowledge from all domains. A combination of mathematics, statistics, and algorithms give rise to data science. The job of a data scientist is not only to create a hypothesis, but also to find data which proves the formulated hypothesis to be correct. Thus, all these elements make the study of data science unique and challenging to master. However, with the right guidance made available through data scientist courses, an aspiring individual can surely reach the pinnacle of the data science industry.

What is a full-stack Data Scientist?

Posted on by Imarticus Learning

 

The world of facts, figures, data, numerics, statistics and other technical information needs an artful collection, collaboration, processing, collating and analysis.

Full stack data is what happens when any data gets collected, analysed and applied for all purposes.  The process helps in visualizing the entire stack of data is a systematic manner.

Data stack science is a broad field where statistics and other kinds of information get scientifically analysed and applied. This field is substantially used to management, business and scientific or technological dealings and aspects.

A person with specialist knowledge of numbers, data collection and research can be generally called data scientist.

Data sourcing, researching, stacking, systematization and application is also applied in fact analysis, machine learning, engineering and other technical studies and training.

Imarticus provides high-quality training and education in all fields requiring full data stacking science. The essential ropeworks are taught in a large number of courses offered both online and on-campus sites, such as Pune. 

Data science stacking training can help students get training and specialization in several management and technical skills. Firstly, the learner can understand the basics of management and business.

He can get to understand action taking, decision making and applying execution skills to the application of data. He can be trained to utilize information to maximize profits, to make smarter uses of information and to select and analyze information with relevance and accuracy. 

Data science knowledge is crucial in setting up new business ventures or making new deals. Action plans need to be made with efficiency and with originality. With fact and data stacking, and without a proper understanding of data collection and research, everything can be futile.

Imarticus takes pride in helping students begin their careers in technology, data science and other related fields, The institute offers projects, mentorships and other opportunities as well. The institute believes in the motto ‘learning by doing’. In other words, training is being provided in a practical and hands-on manner.  

The Data Science course is offered in collaboration with Genpact, the Global leader in analytics. The online and offline classroom experience includes 200 hours of training, work experience with renowned companies and projects and other opportunities. Courses cover all topics relevant to the data science, statistics and technological data analysis.

The Imarticus website (www.staging-imarticus.kinsta.cloud ) will provide all the details about the courses and how to apply. Several case studies have also been added for glimpses into what is expected to be taught and understood.

On completion, industry recognized certificates will be provided. The added advantage of being associated with Genpact can be the sure way to get into this field.  All professionals experienced and novice, are welcome into the programme. 

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.

What is a Data Scientist Internship Like?

Posted on by Imarticus Learning

 

Over the past few decades, Data science has emerged as one of the most admired career fields in the world. Currently, it is estimated that 2.5 quintillions bytes of data are produced every day. The value is expected to keep growing and growing in the future. With such a forecast, the high demand for skilled data scientists is expected to stay. There are various sources to acquire data science skills and knowledge. But it is always better to have a real-life experience before the actual job.

The internships are the best source of real-life experience for any profession and the same goes for data science too. This article describes a typical data scientist internship and provides you with a basic idea about it. The actual experience may vary according to the company you go for.

Messy and Complicated Real Life Data Mining Projects
 If you get to work on any data mining project, don’t expect it to be anywhere near the problems you faced in the classroom. The projects you get will be messy and complicated, unlike the controlled environment in the university lecture.

However, with the help of your teammates, you will be able to do all the complex work. It will help you improve your mining skills and provide you with a taste of real-life mining problems. Before heading to an internship, make sure you are equipped with the right level of skills for such messy data.

Being A Trusted member of the Team
 Most companies provide you with excellent exposure and guidance. You will be taken to many meetings and entrusted with various details. This is intended to inspire you to perform better. The meetings you attend within the several departments will help you to understand how the business runs and how departments are interacting with each other. This knowledge is vital to business understanding.

Developing the Essential Data Science Skills
 You will be facing numerous challenges at each stage of every project. It will lead you towards the skills paramount for a career in data science. You will be required to engage with various staff for information and it adds to your communication skill. Through various projects, you will gain experience in many aspects of organisational operations and project management. Some of them are listed below.

• The need for business understanding
• Importance of feasible project plans and aims
• Value of correct data collection methods
• The need for documentation of a project to make it transparent and repeatable
• Importance of having iteration and feedback from the team to ensure the project progress.

During a Data Science Internship, you will gain very valuable technical experience in various segments of data science. The consultancy experience you obtain through tackling real-life problems is also very vital to your data science career. To equip you with all the necessary skill sets to take on such a career, Imarticus is providing a data science prodegree. This Genpact data science course will help you start the data science career on the right foot.