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What Is Data Science & Why It Is The Most Demanding Skill Now a Days?

Do you want to become a high-profiled professional who is extremely in demand? Do you have a knack for uncovering the hidden figures and trends? Or are you interested in getting a highest paying job that offered immense work satisfaction and not at all boring?

If all or any one of the answers is yes or even if you want to do something that challenges your skills and intellect, why not enroll in Madrid Software, a leading data science institute in London.

Data science remains one of the most promising and in-demand career pathways for qualified individuals.

Data Science course in London showing an effective way for data professionals to recognize that they must go beyond the traditional capabilities of large-scale data analysis, data mining, and programming.

Data scientists must master the complete spectrum of the data science course in London and possess a level of flexibility and awareness to maximize returns at each stage of the process to unearth meaningful insight for their business.

Data Science Training in London

If you are interested in becoming a data scientist, the data science course in London can help you get started on your path to a rewarding career in this exciting and growing sector.

Simply stated, data science is the study of data. Whether you are doing data science courses in London or elsewhere, the course will teach you about methods, tools, and technology to extract, analyze, manage, visualize and store the data for creating in-depth information.

These insights can be created using structured and unstructured data, which are then used to develop data-driven powerful decisions for the business.

Data science is also a multidisciplinary field with roots in computer science, statistics, and math’s. With an abundance of data and lucrative pay-scale, this is one field to consider if you are looking to get into a profession that is in-demand and easy to get employed.

So if you want to enroll in a data science institute in London or elsewhere, there are few things that you should know or want to know. Some of the plaguing questions are the job opportunities, salary, skill set required, and resume employers are looking into when hiring data scientists.

Role and Responsibilities of a Data Scientist:

The data scientist collaborates closely with business stakeholders to learn about their objectives and how data may help them to achieve the desired results.

They construct algorithms and prediction models to extract the business needs and help evaluate the data and share findings with peers. While each project is unique, the following is a broad outline of the data collection and analysis process:

  • • Begin the discovery process by asking the correct questions.
  • • Gather information
  • • Cleanse and process the data
  • • Compile and save data
  • • Data inquiry and exploratory data analysis are the first steps in the data analysis process.
  • • Pick one or more potential models and algorithms to work with.
  • • Use data science approaches like machine learning, statistical modeling, and artificial intelligence to solve problems.
  • • Evaluate and improve outcomes
  • • Inform stakeholders about the final outcome.

Job Titles for Data Scientists:

The following are some of the most prevalent data scientist career:

  • Data scientists: Create algorithms and predictive models using data modeling procedures and bespoke analyses.
  • Data analysts: Work with big data sets to find trends and draw relevant findings that may be used to guide strategic business decisions.
  • Data engineers : Clean, aggregate, and organize data from various sources before moving it to data warehouses.

Typically a data scientist’s work is used by every department of a business, and hence they need to work in close ties with every division.

So, if you want to forge your career in data science, look for a data science course in London with placement. With data scientists no longer limited to e-commerce, there are several carriers that you can work on in different fields as data scientists.

Apart from the above Big Three data scientist roles, other job opportunities that an individual can get after completing a data science course in London with placement are:

  • • Machine Learning Engineer – Avg salary USD 144,813
  • • Quantitative Analyst – Avg Salary USD 127,438
  • • Data Warehouse Architect – Avg Salary USD 134,374
  • • Business Intelligence Analyst – Avg Salary USD 95,806
  • • Statistician – Avg Salary USD 99,286
  • • Business Analyst – Avg Salary USD 80,025
  • • System Analyst – Avg Salary USD 79,469
  • • Marketing Analyst – Avg salary USD 66,379
  • • Operation Analyst – Avg Salary USD 67,254

If you are planning to get your data science certification to boost your career further? It is a good idea because data science is now one of the top jobs globally with a high pay scale.

But before you join data science training in London or anywhere else, whether online or offline, you need to make sure the training offers you the opportunity with case studies and capstone projects and data science interview questions.

Organizations look into these two things along with the data science certification when hiring a data scientist.

What Are Case Studies and Capstone Projects?

Case Studies
Suppose we put it plainly for those who are not familiar with case studies. In that case, it can be described as a systematic, intensive investigation of data, or it can be of any unit. In this, the researcher or analyst examines the data in-depth and relates it to different variables to understand its impact.

Capstone Projects
These are standalone projects taken up by data scientist students or practitioners that can showcase their skills in integrating, synthesizing, and demonstrating their knowledge in data science in a multi-faceted way. Its purpose is to showcase the individual’s readiness to use the data science knowledge in real-life scenarios.

Whether you are enrolled in data science courses in London or elsewhere, ensure that it offers a case study and capstone projects, like Madrid Software, for better hiring prospects as data scientists.

Data scientists are now becoming invaluable parts of an organization that wishes to keep its edge over its competitors. So, if you want to join this elite team of professionals in the market, why not join the Data Science course in London offered by Madrid Software?

What is the course fee for Data Science?

The data science course fees depend on the schedule, institute, and the level of degree offered by the institute.

On average, it can be from Rs.35000/- an online or offline certification course to INR 5 lakhs when done from a management school for two years.

With Madrid Software, you can have a much different solution with an impressive result that is also pocket-friendly.

Over the years, Madrid Software trained Data Scientists have got placement in some of the largest and famous companies and organizations.

Why Choose Madrid Software for data science course in London?

It was established by experienced ex-employees of Cognizant and Infosys who have worked in the field for over nine years. From the time it was founded, they have successfully trained over 20,000 professionals both offline and online.

The data science course developed for Data Scientists focuses on developing real-world projects, multiple case studies, and capstone projects with the most advanced curriculum offered in India.

At Madrid Software, one can apply for a 3-months advanced data science course, 6-months master's program, and 12 months PG program.

So, why wait anymore? Boost your career to a new level with Madrid Software Data Science programs.

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Data Science Course Highlights !



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Course Outline

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Our Data Science Course Is Designed By Industry Experts That Gives The Candidate an Edge In The Market



  •   Introduction To Data Science

    •   Python Concepts

    •   Analytics Concepts Of Python

    •   Numpy Package

    •   Introduction To Pandas

    •   Data Manipulation Using Pandas

    •   Pandas Package

    •   Data Munging With Pandas

    •   Data Visualization With Matplotlib

    •   Data Cleaning Techniques

    •   Predictive Modeling Concepts

    •   Machine Learning Concepts

    •   Statistics

    •   SQL

    •   Unsupervised Learning

    •   Supervised Learning

    •   principal component analysis

    •   Random Forest

    •   Support Vector Machine

    •   Tableau

    •   Case Studies

    •   Capstone Project


Case Studies



YouTube: Analyse and Predict Top Trending Videos for Each Category.
YouTube using Machine Learning based predictive modelling techniques to identify the top trending videos for a particular location based on the results achieved through analyzing the no. of likes, subscription and text mining the key words in user comments and no. of shares over internet.

Tesla Driver Less Cars: Artificial intelligence.
The current AI technologies in Tesla cars are based on unsupervised machine learning which impart decision making capabilities in driver less cars using chips and sensors. It aims to enable cars to navigate through freeways and even traffic on its own.

Zomato: Pick Best Restaurants of the City.
Zomato using predictive modelling machine learning techniques to identify the best resturant in metropolitan cities by analysing the key performance indicators like customer like, mapping positive feedback through text mining, user feedback ratings and type of cuisines served at the resturant.

Netflix: Machine Learning Project on Recommendation System .
Netflix Recommendation systems collect customer data and auto analyze this data to generate customized recommendations for the customers. These systems rely on both implicit and explicit data and based on the pattern present in the data the system provides recommendation to user.



Job Profile And Salaries In Data Science



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Data Science Interview Q & A


1.What is logistic regression in Data science?

Logistic regression measures the relationship between the dependent variable (our label of what we want to predict) and one or more independent variables (our features) by estimating probability using its underlying logistic function (sigmoid). Logistic Regression is also called as the logit model. It is a method to forecast the binary outcome from a linear combination of predictor variables.

2.Differentiate between univariate, bivariate, and multivariate analysis ?

  • Univariate - Univariate data contains only one variable. The purpose of the univariate analysis is to describe the data and find patterns that exist within it.

  • Bivariate - Bivariate data involves two different variables. The analysis of this type of data deals with causes and relationships and the analysis is done to determine the relationship between the two variables.

  • Multi-variate - Multivariate data involves three or more variables, it is categorized under multivariate. It is similar to a bivariate but contains more than one dependent variable.

3.How does data cleaning play a vital role in the analysis?

Dirty data often leads to the incorrect inside, which can damage the prospect of any organization. For example, if you want to run a targeted marketing campaign. However, our data incorrectly tell you that a specific product will be in-demand with your target audience; the campaign will fail.

4.What is the difference between supervised and unsupervised machine learning?

Supervised machine learning – It used unknown and labeled data. It has a feedback mechanism. The most commonly used supervised ML algorithms are decision trees, logistic regression, and support vector machines.

Unsupervised machine learning – It doesn’t require labeled data. Unlike supervised machine learning, it has no feedback mechanism. k-means clustering, hierarchical clustering, and apriori algorithm are the most commonly used unsupervised algorithms.

5.Explain the Decision Tree algorithm in detail?

A decision tree is a popular supervised machine learning algorithm. It is mainly used for Regression and Classification. It allows breaks down a dataset into smaller subsets. The decision tree can able to handle both categorical and numerical data.

6.What do you understand by the term recommender systems? Where are they used?

A subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product are recommender systems. Recommender systems are widely used in movies, news, research articles, products, social tags, music, etc. It helps you to predict the preferences or ratings which users likely to give to a product.

7.What is the p-value? What is its importance?

When you conduct a hypothesis test in statistics, a p-value allows you to determine the strength of your results. It is a numerical number between 0 and 1. Based on the value it will help you to denote the strength of the specific result.

p-value typically ≤ 0.05 shows strong evidence against the null hypothesis; so you reject the null hypothesis.

p-value typically > 0.05 shows weak evidence against the null hypothesis, so you accept the null hypothesis.

p-value at cutoff 0.05, this is considered to be marginal, meaning it could go either way.

8.We want to predict the probability of death from heart disease based on three risk factors: age, gender, and blood cholesterol level. What is the most appropriate algorithm for this case?

Choose the correct option:

  • Logistic Regression
  • Linear Regression
  • K-means clustering
  • Apriori algorithm

  • The most appropriate algorithm for this case is A, logistic regression.

9.Below are the eight actual values of the target variable in a train file. Find out the entropy of the target variable.

[0, 0, 0, 1, 1, 1, 1, 1]
Choose the correct answer.

  • -(5/8 log(5/8) + 3/8 log(3/8))
  • 5/8 log(5/8) + 3/8 log(3/8)
  • 3/8 log(5/8) + 5/8 log(3/8)
  • 5/8 log(3/8) – 3/8 log(5/8)

The target variable, in this case, is 1.
The formula for calculating the entropy is:
Putting p=5 and n=8, we get
Entropy = A = -(5/8 log(5/8) + 3/8 log(3/8))

10.Why do you want to be a data scientist?

The answer may vary from person to person. The aim is, to be honest, and polite. You may answer this like this. “I have a passion for working for data-driven, innovative companies. Your firm uses advanced technology to address everyday problems for consumers and businesses alike, which I admire. I also enjoy solving issues using an analytical approach and am passionate about incorporating technology into my work.”

FAQ


1. Who Should Do a Data Science Course?

Beginners and working professionals, both are eligible to do pg program in data science. To become a data scientist, you could earn a Bachelor's degree in Computer science, Social sciences, Physical sciences, and Statistics. You need to know programming languages like Python, Perl, C/C++, SQL, and Java.

2.What Are The Most Valuable Skill For a Data Science Professional ?

The most valuable skills for data science professionals are as follows:

  • Probability & Statistics
  • Multivariate Calculus & Linear Algebra
  • Programming, Packages, and Software
  • Data Wrangling
  • Database Management
  • Data Visualization
  • Machine Learning / Deep Learning
  • Cloud Computing
  • Microsoft Excel
  • DevOps

3. Is This Courses Useful For Non-Tt Professional

Any person with a structural thought process, good logical thinking skills, conviction towards learning new tools, and with a good business perspective can get into the field of data sciences. It’s not exceptional coders or highly knowledgeable people that are required.

4. What Is The Average salary Of a Data Scientist

The salary depends upon the company you are entering. The average data scientist’s salary is ₹698,412. As your experience and skills grow, your earnings rise dramatically as senior-level data scientists around more than ₹1,700,000 a year in India!

5. What Are The Top Algorithms That Every Data Science Professional Must Know

The top algorithms are:

  • Decision Tree
  • Logistic Regression
  • Linear Regression
  • SVM (Support Vector Machine) ...
  • Naive Bayes
  • KNN
  • K-Means Clustering
  • Random Forest
  • Dimensionality Reduction Algorithms
  • Neural Network

6. How Much Math In Statistics Is Used In Data Science

Math and Statistics for Data Science are essential because these disciples form the basic foundation of all the Machine Learning Algorithms. But, practical data science doesn't require very much math at all. It only requires skill in using the right tools. In statistics, you should know about probability distributions, statistical significance, hypothesis testing, and regression.

7. Which Programming Language Is Most Widely Used For Data Science

Python is the most popular and widely used data science programming language in the world today. It is an open-source, easy-to-use language that has been around. This general-purpose and vibrant language is innately object-oriented. It also ropes numerous paradigms, from functional to structured and procedural programming.

8.What Are The Top Companies In India To Work For After Completing Data Science Course

Many companies in India recruit Data Science professionals from entry-level to higher positions. Some of the top recruiters of Data Science and Big Data professionals in India for which you can work after completing the data science course are Equifax, Accenture, Amazon, Deloitte, LinkedIn, MuSigma, Flipkart, IBM, Citrix, Myntra, Juniper Network, etc

9. Do We Get Placement Support After Completing The Course

Yes, Madrid Software Trainings provide 100% placement support after the course and don’t throw at the deep end!

10.Do We Get Online Training Also In Data Science From Madrid Software Trainings

Yes, Madrid Software Trainings also provides online training for data science.

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