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6 Essential Skills Data Scientists Need to Add to Their Resumes

6 Essential Skills Data Scientists Need to Add to Their Resumes

Like all other career paths, cracking the hottest job of 21st century is mainly about gaining knowledge and developing important skills relevant to the job. And your resume should reflect all these skills. So what must the resume of a professional data scientist look like? Here are 6 key skills that must be in the fingertips of a good data scientist.

Stats and Math:

Not only blue-chip tech companies, even medium and small scale enterprises are operated by data science these days. And statistical knowledge is vital for that. You should be thorough with general statistical concepts, like distributions, tests, range, likelihood estimators, etc.

In mathematics, one must know the basics of linear algebra and multivariable calculus. This will definitely make a difference in your work outcomes as it enables you to improve predictive presentations.

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Excellent Programming and Computing Skills:

Simply put, being good at coding is a must. So, if you are a budding data scientist you must actively work towards developing a computing mind; you should be able to understand, write and even analyze code whenever necessary. This level of dexterity only comes through meticulous study and practice of not one, but a number of programming languages.

If you want to develop a programming skill which is especially designed for data scientists, then get enrolled for R programming certification. Over 40 percent data scientists prefer R for solving stat problems. But it must be noted that R isn’t easy to learn, especially for those who aren’t comfortable with codes.

Python is another language which is highly preferred by data scientists because it is very adaptable and hence, can be employed in all the different steps part of a data science project. Moreover, data sets can be created with ease and SQL tables can be imported into working codes when required. Considering these benefits and the fact that over 50% data scientists favor Python, an excellent Python Certification in Delhi should be first in your list of courses to undertake.

Live Projects

Learning isn’t effective unless you implement it practically. Moreover, your skills get duly appreciated when it’s demonstrated. Hence, always look for live projects you can join and try to understand the data architecture behind the screen. It may be up there in your head, but it needs to be implemented. Large companies actually prefer candidates who have more practical experience rather than just bookish knowledge.

Managing Unstructured Data

Unstructured data is any type of content that doesn’t fit into traditional database tables. These data types aren’t well organized and hence, sorting them becomes very difficult. Blogs, videos and customer reviews are some examples of unstructured data. Being able to manage unstructured data is an important skill for data scientists. Apache Hadoop, NoSQL and Microsoft HDI insight are some good software for tackling unstructured data. If you are interested to learn the techniques, you can look up the course details for Hadoop certification in Delhi at DexLab Analytics.

Storytelling with Data

Data scientists might have to work with complicated models and datasets, but they must know how to express their deductions in lucid language that’s simple and engaging. Hence their raw data must be expressed in the form of tables, charts and graphs, which are visually appealing and can capture the attention of stakeholders.

Academics and Degrees

A strong educational background is the door to the world of data science. Big companies prefer applicants who are master degree holders in either stats or math or computer science or physical science.

Data science is definitely the trendiest job and you might be eager to land one, but it’s not easy to acquire the above mentioned skills. If you are looking for guidance from experts who have previously worked in this field, then you should get enrolled for Data Science Courses in Delhi right away. The industry experts at DexLab Analytics tailor the courses to the unique needs of students and incorporate ample practical cases to help them get ready for the challenges ahead.

 

Reference: www.analyticsindiamag.com/7-things-data-scientists-must-have-in-their-resumes

 

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Top Python Interview Questions You Should Start Preparing RN

Top Python Interview Questions You Should Start Preparing RN

Welcome! May be, you are new to programming altogether or seasoned in the field; either way, you have come to the right place, and have chosen the right language. Python is a very powerful, advanced kind of object-oriented programming language. It is simple and follows an easy to use syntax. Therefore, it has stood out to be the most popular programming language to learn and master, even by those who has set foot recently into the world of computer programming.

So, if you are planning to kickstart a career in Python, pore over these frequently asked questions; they are often asked in job interviews. If you have any doubts regarding Python, feel free to ask us, or comment below.

Or you can also take up our Python Course in Delhi NCR. It’s industry-related and student-friendly.

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What is Python?

Highly readable, interactive and interpreted, Python is an object-oriented scripting language. It focuses on English language, while its competitors use punctuations more.

What are the key features of Python?

  • Dynamically typed – In Python, you don’t have to state the variable kinds. You can easily run x=111  and then x="I'm a string" without error.
  • Interpreted language – This means Python doesn’t have to be compiled before running. PHP and Ruby are other interpreted languages.
  • Object oriented programming – Python is highly suitable for such kind of programming. It lacks access specifiers (like C++’s public, private>).

What role does PYTHONPATH environment variable plays?

PYTHONPATH works similar to a PATH. It helps Python interpreter in locating the module files imported into a program. It includes Python source library directory as well as the directories comprising the Python source code.

Name the supported data types in Python.

Python has 5 supported data types:

  • Numbers
  • List
  • String
  • Dictionary
  • Tuple

Do you know how to display contents of text file in a reverse order?

  • Change the given file into a list,
  • Then reverse the list by using reversed()
  • Eg: for line in reversed(list(open(“file-name”,”r”))):
  • Print(line)

Pick out the invalid statement from below.

  1. a) abc = 1,000,000
  2. b) a b c = 1000 2000 3000
  3. c) a,b,c = 1000, 2000, 3000
  4. d) a_b_c = 1,000,000

        Answer: b

How do you manage memory in Python?

  • Python private heap space manages its memory. All Python items and data structures are stored in a private heap. The programmer is not given access to this, but an interpreter manages this heap.
  • Pythom memory manager allocates Python heap space, while giving some access to the programmer for coding.
  • Python comprises of an inbuilt garbage collector – it recycles all the redundant memory, frees the extra memory and feeds it into the heap space.

What do you mean by Python dictionary?

Python dictionary is nothing but built-in datatypes. It entails a one-to-one relationship between keys and values. Dictionaries are indexed by keys; they contain pairs of keys and their corresponding values.

Hope this set of Python interview questions have prepared you well for upcoming job interviews. All the best!

Want to learn Python and its whole set of applications? Feel free to check out our interactive Python certification course training, offered by industry experts. They help transform your career.

 

The blog has been sourced from:

www.edureka.co/blog/interview-questions/python-interview-questions

intellipaat.com/interview-question/python-interview-questions

 

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5 Big Challenges That Data Scientists Face Each Day

5 Big Challenges That Data Scientists Face Each Day

Data is lucrative; the world is revolving around how we churn out data. As a result, there’s been a high demand for data scientists. But of course, as rightfully said there’s no gain without pain – the promising field of data science is laden with many challenges, which needs to be overcome by expert consultants under needful guidance and with deft expertise.

Below, we’ve mentioned top 5 data science challenges, and how to handle them well…

Address the Specifics

Successful data scientists don’t try to do everything on their own. Instead, they individually focus on a single specific area. “I would encourage new professionals to understand that data science is a bit like medicine—it’s a vast and vague term that encapsulates wildly different practices under one roof,” said Tal Kedar, CTO at Optimove. “Data scientists [can have] very different engineering skill sets [and be] experienced with very different platforms and tools.”

For data science certification, look no further. DexLab Analytics is a prime data science training institute catering to the needs of enthusiast students. 

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Be Guided By Your Intuition

Being a data scientist not only exposes you to the question of ‘how’, but also ‘why’. No longer do you just sift through data to make connections, instead you have to use your comprehensive knowledge to develop ‘mental model’, which can be accepted or rejected by your data.

Cross-Department Expertise is Appreciable

“The best data scientists are not just statisticians or machine learning experts; they are also an authority in the field or business where they are applying those skills,” said Kedar. It’s no hard fact, data scientists are arguably the best bridge between technical and non-technical teams. Quite naturally, whichever career they chose next, their skills will be treated as an asset to the next company in question.

Seamless Flow of Communication

Communication amongst the data teams is crucial – data scientists need to explain technical concepts to audiences from other departments, including executives and stakeholders, who might not belong from technical backgrounds. “It can be exciting to share all of the technical complexities that got you to your conclusions,” said Andrew Seitz, senior data analyst at Snowflake. “But what your stakeholders need are the key findings and action items. Save the details for the appendix (or Q&A).”

Raw Data Play

The biggest challenge for data scientists is to find ways of using the data – how the process of data extraction, data cleaning, data analysis and data modeling are carried out. Data scientists need to possess broad domain expertise in all programming languages, such as Python, R and SQL.

The work life of a data scientist revolves around creating clean data sets loaded with useful information on which machine learning algorithms can be applied. This kind of job is mostly treated as an art instead of science, because a majority of hard work and effort goes unnoticed when observing the final product, just like an artist’s craft.

The scope and capability of data science is encompassing, so are the challenges. But, of course, most of the challenges can be mitigated with considerable preparation and communication. How? With an intensive Python data science course – from the expert consultants of DexLab Analytics.

 

The blog has been sourced fromwww.forbes.com/sites/laurencebradford/2018/09/06/8-real-challenges-data-scientists-face/#8adbc206d999

 

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3 Most Used Data Science Tools in 2018

The humongous amount of data calls for advanced data science tools – to completely understand and analyze the information.

Data analytics fuels digital transformation. The best way to do this is by arming an expert pool of statisticians, math pundits and business analysts with suitable data science tools with which they can squelch out crucial insights from the ever-growing silos of corporate data. This kind of initiatives promote a data-driven business culture, which acts as a present prerequisite – and this why here we’ve jotted down top 3 data science tools that’s weaving wonders with the new oil of the world, data:

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Python

Both, well-performing software and a powerful programming language perfect for developing custom algorithms, Python is the most must-have tool for all data scientists. In a recent KDnuggets survey of 2052 users, Python language was recommended by 65.6% of respondents.

“We use Python both for data science and back end, which provides us with rapid development and machine learning model deployment,” shared Alexander Osipenko, lead data scientist at Cindicator Inc. “It’s also of great importance for us to ensure the security of implemented tools.”

Leslie De Jesus, innovation director and lead data scientist at Wovenware emphasized on the importance of Python libraries. “[We use] Python Libraries, including Scrapy, for web scraping and being able to extract data from the internet and upload it into a data frame for analysis,” said De Jesus.

Few others vouched for Python because of its multifaceted nature and strong optimization skills.

For Python Certification Training in Delhi, drop by DexLab Analytics.

R

Quite similar to Python, R is the go-to programming language for many data scientists and they depend on it wholly because it’s simpler and more specifically-built for data science. According to the KDnuggets poll, 48.5% respondents voted it to be one of the leading data science tools.

As for all, R programming language is blessed with cultivated capabilities for machine learning and statistics, and professionals love using it. It’s another favorite of data analysts, especially those who deals with a lot of data exploration.

“I can quickly see summary stats like mean, median and quartiles; quickly create different graphs; and create test data sets, which can be easily shared and exported to CSV format,” said Jon Krohn, chief data scientist at Untapt Inc.

Seeking R language certification in Delhi? We have DexLab Analytics for you!

Tableau

Bridging the gap between skilled data science teams and more business-oriented analytics consultants, Tableau Software is the fastest data visualization and dashboard tool. “It is a fantastic tool for data scientists and noobs working on data science,” said Pooja Pandey, senior executive for SEO at Entersoft Security. “[It’s a] quick dashboarding tool to visualize insights and analytical data with a very short learning curve.”

The lightening speed of Tableau’s visualization and reporting functions is commendable. It’s easy to learn, quick to implement and intuitive to use. Moreover, it helps different segments of a company to customize exhaustive reports according to their requirements.

Now, if you are looking for ways to hone your visualization skills, we would recommend Tableau BI training courses from DexLab Analytics. Their training courses are comprehensive, well-research and as per industry standards.

 

The blog has been sourced fromsearchbusinessanalytics.techtarget.com/feature/Data-scientists-weigh-in-5-data-science-tools-to-consider

 

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