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Statistical Application In R & Python: Chapter 1 – Measure Of Central Tendency

Posted on July 30, 2019July 31, 2019 by Dexlab

Statistical Applcation In R & Python: Chapter 1 – Measure Of Central Tendency

Statistical analysis helps explore data relationship and develop high-end models to frame better decisions. It’s an intricate process of collecting and evaluating data to define the nature of data that has to be analyzed.

Below, we dig into the basics of statistical application in R and Python using the measure of central tendency.

Introduction:-

As body methods for the study of numerical data, if some rows or columns are too long, in such cases, it becomes necessary to summarize data in an easily manageable form. The purpose is to serve by classifying the data in the form of frequency distribution and various graphs. When data relate to a variable, the process of summarization can be taken a step further by using certain descriptive measures. The dim is to focus on certain features that are central frequency and description.

Central Tendency :

In a set of data, they have a tendency, notwithstanding their variability, to cluster-around a central value and the tendency of the quantitative statistical observations is called central tendency.

The three measures of the central tendency are commonly used is:-

Mean
Median
Mode

The description of these 3 estimators start below:-

Mean:-

Mean is the average of central tendency and is the most commonly used measures.

The concept of mean is divided into three parts:-

Arithmetic mean.
Geometric mean.
Harmonic mean.

Mainly the mean refers to an arithmetic mean.

Arithmetic Mean (A.M.):-

The arithmetic mean of a set of observations is defined to be their sum, divided by the number of observations.

For n numbers of observation (x₁,x₂,… ,x_n )

Weighted A.M.

For frequency distribution where have frequencies. (i=1,2,3…)

Application of A.M.:-

Let’s, calculate the mean of Age, Height & Weight from the given data.

Name	Sex	Age	Height	Weight
Ritesh	M	24	6.9	112.5
Heena	F	23	5.65	84
Kritika	F	23	6.53	98
Anuradha	F	24	6.28	102.5
Gaurav	M	24	6.35	102.5
Prakash	M	22	5.73	83
Aarti	F	22	5.98	84.5
Meena	F	25	6.25	112.5
Utkarsh	M	23	6.25	84
Chirag	M	22	5.9	99.5
Neha	F	21	5.13	50.5
Smrita	F	24	6.43	90

Calculating Mean in Python:

Therefore,

Age (Mean) = 23.08333333, Height (Mean) = 6.12, weight(Mean) = 85.625

Calculating Mean in R:

Application of Weighted A.M.:-

The weighted mean is denoted that the mean with frequency.

Data to solve:

Calculate the average price per ton of coal purchased by the industry for the half-year.

Month	Price Per Ton	Tons Purchased
January	Rs. 52.49	26
February	Rs. 62.23	34
March	Rs. 87.26	40
April	Rs. 45.25	54
May	Rs. 78.56	13
June	Rs. 69.25	45

Data to solve:

Month	Price (Rs) Per Ton (x)	Tons Purchased (f)	fx=y (Main Data)
January	52.49	26	1364.74
February	62.23	34	2115.82
March	87.26	40	3490.4
April	45.25	54	2443.5
May	78.56	13	1021.28
June	69.25	45	3116.25
Total	395.04	N=212	13551.99

The price is denoted as x (52.49, 62.23, 87.26, 45.25, 78.56, 69.25 [in Rs.])=395.04

The amount of purchased (frequency) is denoted by f (26, 34, 40, 54, 13, 45) = 212 (N)

Then multiply the x and f and we get the total amount which is denoted by y, fx(y) = 13551.99

Calculate Weighted Mean in R:

Calculate Weighted Mean in Python:

To calculate the weighted mean from R & Python we get the same result = 63.9244811.

Want to know more about the nature of data? Keen to perform high-end statistical analysis using Python and R? Follow DexLab Analytics, an excellent Python training center in Gurgaon, India. Our team of consultants will help you learn the basics of R and Python in the easiest manner possible.

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Demand for Data Analysts is Skyrocketing – Explained

Posted on June 6, 2019May 23, 2020 by Dexlab

Demand for Data Analysts is Skyrocketing - Explained

The salary of analytics professionals outnumbers that of software engineers by more than 26%. The wave of big data analytics is taking the world by storm. If you follow the latest studies, you will discover that there has been a prominent growth in median salary over several experience levels in the past three years (2016 to 2018). In 2019, the average analytics salary has been capped at 12.6 lakh per annum.

The key takeaway is that the salary structure of analytics professionals continues to beat other tech-related job roles. In fact, data analysts are found out-earning their Java correspondents by nearly 50% in India alone. A latest survey provides an encompassing view of base and compensation salaries in data science along with median salaries followed across diverse job categories, regions, education profiles, experience, tools and skills.

In this regard, a spokesperson of a prominent data analytics learning institute was found saying, “The demand for AI skills is expected to increase rapidly, which is also reflected by the fact that AI engineers command a higher salary than peers.” She further added, “Many of our clients have realized that investing in data-driven skills at the leadership level is a determining factor for the success of digital and AI initiatives in the organization. With the increasing adoption of digital technologies, we expect an enduring growth of Data Science and AI initiatives to offer exciting and lucrative career options to new age professionals,”

Over time, we are witnessing how markets are evolving while the demand for skilled data scientists is following an upward trend. It is not only the technology firms that are posting job offers, but the change is also evident across industries, like retail, medical, retail and CPG amongst others. These sectors are enhancing their analytical capabilities implying an automatic increase in the number of data-centric jobs and recruitment of data scientists.

Points to Consider:

In the beginning, nearly 76% of data analysts earn 6-lakh figure per annum.
The average analytics salary observed in 2018-19 is 12.6 lakh.
In terms of analytics career, Mumbai offers the highest compensation of 13.7 lakh yearly, followed by Bangalore at 13 lakh.
Mid-level professionals proficient in data analytics are more in demand.
Knowing Python is an added advantage; Python Programming training will help you earn more. Expect a package of 15.1 lakh.
Nevertheless, we often see a pay disparity for female data scientists against their male counterparts. While women’s take-home salary is 9.2 lakh, male from the same designation and profession earns 13.7 lakh per annum.

As endnotes, the demand for data science skills is skyrocketing. If you want to enter into this flourishing job market, this is the best time! Enroll in a good data analyst course in Delhi and mould your career in the shape of success! DexLab Analytics is a top-notch data analyst training institute that offers a plethora of in-demand skill training courses. Reach us for more.

This article has been sourced from — www.tribuneindia.com/news/jobs-careers/data-analytics-professionals-ride-the-big-data-wave/759602.html

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Top 4 Python Industrial Use-Cases: Explained

Posted on April 16, 2019March 14, 2020 by Dexlab

Top 4 Python Industrial Use-Cases: Explained

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Python is one of the fastest-growing and most popular coding languages in the world; a large number of developers use it on daily basis and why not, it works brilliantly for a plethora of developer job roles and data science positions – starting from scripting solution for sysadmins to supporting machine learning algorithms to fueling web development, Python can work wonders across myriad platforms!

Below, we’ve rounded up 4 amazing Python industrial use-cases; scroll ahead:

Insurance

Widely used in generating business insights; courtesy machine learning.

Case Study:

Smaller firms driven by machine learning gave stiff competition to a US multinational finance and insurance corporation. In return, the insurer formed teams and devised a new set of services and applications based on ML algorithms to enjoy a competitive edge. However, the challenge was that with so many data science tools, numerous versions of Python came into the picture and gave rise to compatibility issues. As a result, the company finalized only one version of Python, which was then used in line with machine learning algorithms and tools to derive specific results.

Finance

Data mining helps determine cross-sell opportunities.

Case Study:

Another US MNC dealing in financial services showed interest in mining complex customer behavioral data. Using Python, the company launched a series of ML and data science initiatives to dig into its structured data that it has been gathering for years and correlated it with an army of unstructured data, gathered from social media and web to enhance cross-selling and retrieve resources.

Aerospace

Python helps in meeting system deadlines and ensured utmost confidentiality.

Case Study:

Recently, the International Space Station struck a deal with an American MNC dealing in military, defense and aerospace technology; the latter has been asked to provide a series of systems to the ISS. The critical safety systems were mostly written in languages, like Ada; they didn’t fare well in terms of scripting tasks, data science analysis or GUI creation. That’s why Python was chosen; it offered bigger contract value and minimum exposure.

Retail Banking

Enjoy flexible data manipulation and transformation – all with Python!

Case Study:

A top-notch US department store chain equipped with an in-store banking division gathered data and stored it in a warehouse. The main aim of the company was to share the information with multiple platforms to fulfill its supply chain, analytics, retail banking and reporting needs. Though the company chose Python for on-point data manipulation, each division came up with their own versions of Python, resulting in a new array of issues. In the end, the company decided to keep a standard Python; this initiative not only resulted in amplifying engineering speed but also reduced support costs.

As end notes, Python is the next go-to language and is growing each day. If you have dreams of becoming an aspiring programmer, you need to book the best Python Certification Training in Delhi. DexLab Analytics is a premier Python training institute in Delhi; besides Python, it offers in-demand skill development courses for interested candidates.

The blog has been sourced from— www.techrepublic.com/article/python-5-use-cases-for-programmers

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Now Machine Learning Can Predict Premature Death, Says Research

Posted on April 2, 2019July 11, 2020 by Dexlab

Now Machine Learning Can Predict Premature Death, Says Research

Machine Learning yet again added another feather in its cap; a team of researchers tried and tested a suave machine learning system that can now predict early death. Yes, premature death can now be estimated, courtesy a robust technology and an outstanding panel of researchers from the University of Nottingham! At first, it may sound weird and something straight out of a science fiction novel, but fret not – machine learning has proved itself in improving the status of preventive healthcare and now it’s ready to venture into new unexplored medical territories.

Prediction at Its Best

Published in PLOS ONE in one of their special editions of Machine Learning in Health and Biomedicine, the study delves into how myriad AI and ML tools can be leveraged across diverse healthcare fields. The technology of ML is already reaping benefits in cancer detection, thanks to its sophisticated quantitative power. These new age algorithms are well-equipped to predict death risks of chronic diseases way ahead of time from a widely distributed middle-aged population.

To draw clear conclusions, the team collected data of more than half a million people falling within the age group of 40 and 69 from the UK Biobank. The data collection is from the period 2006-2010, followed up till 2016. With this data in tow, the experts analyze biometric, demographic, lifestyle and clinical factors in each individual subject. Robust machine learning models are used in the process.

Adding in, the team observed dietary consumption of vegetables, fruits and meat per day of each subject. Later, the team from Nottingham University proceeded to predict the mortality of these individuals.

“We mapped the resulting predictions to mortality data from the cohort, using Office of National Statistics death records, the UK cancer registry and ‘hospital episodes’ statistics,” says Dr. Stephen Weng, assistant professor of Epidemiology and Data Science. “We found machine-learned algorithms were significantly more accurate in predicting death than the standard prediction models developed by a human expert.”

Accuracy and Outcome

The researchers involved in this ambitious project are excited to the bones. They are eager about the outcomes. They are in fact looking forward to a time where medical professionals would be able to distinguish potential health hazards in patients with on-point accuracy and evaluate the following steps that would lead the way towards prevention. “We believe that by clearly reporting these methods in a transparent way, this could help with scientific verification and future development of this exciting field for health care”, shares Dr. Stephen Weng.

As closing thoughts, the research is expected to build the foundation of enhanced medicine capabilities and deliver customized healthcare facilities tailoring risk management for each individual patient. The Nottingham research draws inspiration from a similar study where machine learning techniques were used to predict cardiovascular diseases.

In case, you are interested in Machine Learning Using Python training course, DexLab Analytics is the place to be. With a volley of in-demand skill training courses, including Python certification training and AI training, we are one of the best in town. For details, check out our official website RN.

The blog has been sourced from —
interestingengineering.com/machine-learning-algorithms-are-now-able-to-predict-premature-death

General Python Guide 2019: Learning Data Analytics with Python

Posted on February 6, 2019May 23, 2020 by Dexlab

General Python Guide 2019: Learning Data Analytics with Python

Python and data analytics are possibly three of the most commonly heard words these days. In today’s burgeoning tech scene, being skillful in these two subjects can prove very profitable. Over the years, we have seen the importance of Python education in the field of data science skyrocketing.

So here we present a general guide to help start off your Python learning:

Reasons to Choose Python:

Popularity

With over 40% data scientists preferring Python, it is clearly one of the most widely used tools in data analysis. It has risen in popularity above SAS and SQL, only lagging behind R.

General Purpose Language

There might be many other great tools in the market for analyzing data, like SAS and R, but Python is the only trustworthy general-purpose language valid across a number of application domains.

Step 1: Setup Python Environment

Setting up Python environment is uncomplicated, but a primary step. Downloading the free Anaconda Python package is recommended. Besides core Python language, it includes all the essential libraries, such as Pandas, SciPy, NumPy and IPython, and graphical installer also. Post installation, a package containing several programs is launched, most important one being iPython also known as Jupyter notebook. After launching the notebook, the terminal opens and a notebook is started in the browser. This browser works as the coding platform and there’s no need for internet connection even.

Step 2: Knowing Python Fundamentals

Getting familiar with the basics of Python can happen online. Active participation in free online courses, where video tutorials, practice exercises are plentiful, can help you grasp the fundamentals quickly. However, if you are seeking expert guidance, you must explore our Python data science courses.

Step 3: Know Key Python Packages used for Data Analysis

Since it is a general purpose language, Python’s utility stretches beyond data science. But there are plentiful Python libraries useful in data functionalities.

Numpy – essential for scientific computing

Matplotib – handy for visualization and plotting

Pandas – used in data operations

Skikit-learn – library meant to help with data mining and machine learning activities

StatsModels – applied for statistical analysis and modeling

Scipy-SciPy – the Numpy extension of Python; it is a set of math functions and algorithms

Theano – package defining multi-dimensional arrays.

Step 4: Load Sample Data for Practice

Working with sample datasets is a great way of getting familiar with a programming language. Through this kind of practice, candidates can try out different methods, apply novel techniques and also pinpoint areas of strength and in need of improvement.

Python library StatModels contains preloaded datasets for practice. Users can also download dataset from CSV files or other sources on web.

Step 5: Data Operations

Data administration is a key skill that helps extract information from raw data. Majority of times, we get access to crude data that cannot be analyzed straightaway; it needs to be manipulated before analyzing. Python has several tools for formatting, manipulating and cleaning data before it is examined.

Step 6: Efficient Data Visualization

Visuals are very valuable for investigative data analysis and also explaining results lucidly. The common Python library used for visualization is Matplotlib.

Step 7: Data Analytics

Formatting data and designing graphs and plots are important in data analysis. But the foundation of analytics is in statistical modeling, data mining and machine learning algorithms. Having libraries like StatsModels and Scikit-learn, Python provides all necessary tools essential for performing core analyzing functions.

Concluding

As mentioned before, the key to learning data analytics with Python is practicing with imported data sets. So without delay, start experimenting with old operations and new techniques on data sets.

For more useful blogs on data science, follow DexLab Analytics – we help you stay updated with all the latest happenings in the data world! Also, check our excellent Python courses in Delhi NCR.

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Decoding the Equation of AI, Machine Learning and Python

Posted on December 19, 2018June 13, 2020 by Dexlab

Decoding the Equation of AI, Machine Learning and Python

AI is an absolute delight. Not only is it considered one of the most advanced fields in the present computer science realm but also AI is a profit-spinning tool leveraged across diverse industry verticals.

In the past few years, Python also seems to be garnering enough fame and popularity. Ideal for web application development, process automation, web scripting, this wonder tool is a very potent programming language in the world. But, what makes it so special?

Owing to ease of scalability, learning and adaptability of Python, this advanced interpreted programming language is the fastest growing global language. Plus, its ever-evolving libraries aid it in becoming a popular choice for projects, like mobile app, data science, web app, IoT, AI and many others.

Python, Machine Learning, AI: Their Equation

Be it startups, MNCs or government organizations, Python seem to be winning every sector. It provides a wide array of benefits without limiting itself to just one activity – its popularity lies in its ability to combine some of the most complex processes, including machine learning, artificial intelligence, data science and natural language processing.

Deep learning can be explained as a subset of a wider arena of machine learning. From the name itself you can fathom that deep learning is an advanced version of machine learning where intelligence is being harnessed by a machine generating an optimal or sub-optimal solution.

Combining Python and AI

Lesser Coding

AI is mostly about algorithms, while Python is perfect for developers who are into testing. In fact, it supports writing and execution of codes. Hence, when you fuse Python and AI, you drastically reduce the amount of coding, which is great in all respects.

Encompassing Libraries

Python is full of libraries, subject to the on-going project. For an instance, you can use Numpy if you are into scientific computation – for advanced computing, you have put your bet on SciPy – whereas, for machine learning, PyBrain is the best answer.

A Host of Resources

Entirely open source powered by a versatile community, Python provides incredible support to developers who want to learn fast and work faster. The huge community of web developers are active worldwide and willing to offer help at any stage of the development cycle.

Better Flexibility

Python is versatile. It can be used for a variety of purposes, right from OOPs approach to scripting. Also, it performs as a quintessential back-end and successfully links different data structures with one another.

Perfect for Today’s Millennial

Thanks to its flexibility and versatility, Python is widely popular amongst the millennials. You might be surprised to hear that it is fairly easier to find out Python developers than finding out Prolog or LISP programmers, especially in some countries. Encompassing libraries and great community support helps Python become the hottest programming language of the 21^st century.

Some of the most popular Python libraries for AI are:

AIMA
pyDatalog
SimpleAI
EasyAI

Want to ace problem-solving skills and accomplish project goals, Machine Learning Using Python is a sure bet. With DexLab Analytics, a recognized Python Training Center in Gurgaon, you can easily learn the fundamentals and advance sections of Python programming language and score goals of success.

The blog has been sourced from ― www.information-age.com/ai-machine-learning-python-123477066

5 Big Challenges That Data Scientists Face Each Day

Posted on September 21, 2018May 23, 2020 by Dexlab

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.

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 from — www.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

Posted on September 20, 2018May 23, 2020 by Dexlab

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:

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 from — searchbusinessanalytics.techtarget.com/feature/Data-scientists-weigh-in-5-data-science-tools-to-consider

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Introduction:-

Central Tendency :

Mean:-

Arithmetic Mean (A.M.):-

Weighted A.M.

Application of A.M.:-

Calculating Mean in Python:

Calculating Mean in R:

Application of Weighted A.M.:-

Data to solve:

Calculate the average price per ton of coal purchased by the industry for the half-year.

Data to solve:

Calculate Weighted Mean in R:

Calculate Weighted Mean in Python:

Interested in a career in Data Analyst?

Points to Consider:

Interested in a career in Data Analyst?

Insurance

Widely used in generating business insights; courtesy machine learning.

Finance

Data mining helps determine cross-sell opportunities.

Aerospace

Python helps in meeting system deadlines and ensured utmost confidentiality.

Retail Banking

Enjoy flexible data manipulation and transformation – all with Python!

Interested in a career in Data Analyst?

Prediction at Its Best

Accuracy and Outcome

Classroom or Online Certification Courses to get you started

Reasons to Choose Python:

Popularity

General Purpose Language

Step 1: Setup Python Environment

Step 2: Knowing Python Fundamentals

Step 3: Know Key Python Packages used for Data Analysis

Step 4: Load Sample Data for Practice

Step 5: Data Operations

Step 6: Efficient Data Visualization

Step 7: Data Analytics

Concluding

Interested in a career in Data Analyst?

Python, Machine Learning, AI: Their Equation

Combining Python and AI

Lesser Coding

Encompassing Libraries

A Host of Resources

Better Flexibility

Perfect for Today’s Millennial

Some of the most popular Python libraries for AI are:

Classroom or Online Certification Courses to get you started

What is Python?

What are the key features of Python?

What role does PYTHONPATH environment variable plays?

Name the supported data types in Python.

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

Pick out the invalid statement from below.

How do you manage memory in Python?

What do you mean by Python dictionary?

The blog has been sourced from:

Interested in a career in Data Analyst?

Address the Specifics

Be Guided By Your Intuition

Cross-Department Expertise is Appreciable

Seamless Flow of Communication

Raw Data Play

Interested in a career in Data Analyst?

Python

R

Tableau

Interested in a career in Data Analyst?

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