Data Science Certification Training in 30 days

  • Become a Data Scientist by getting project experience
  • Stay updated in your career with lifetime access to live classes
  • Connect with recruiters through video project portfolios

About Data Scientists Training Course

DeZyre’s Data Scientist Courses will prepare you for the job role of a data scientist and will help you gain data scientist skillset by learning data science using analytical tools like Python and R. This data scientist course will help you master analytical techniques like data exploration, data visualization and various predictive analytic techniques by implementing real-life, industry-oriented data science projects using Python data science programming language. This data science course will also help you gain expertise about various popular machine learning algorithms like Decision Trees, K-mean Clustering, Gradient Boosting, Boosted Trees, Random Forest, and Naïve Bayes using Python programming language. This data science specialization is best suited for beginners and also experienced professionals who would like to use Python for doing data science.

Data Science Project Portfolio

Build an online data science project portfolio with your project code and video explaining your data science project. This is shared with recruiters.

32 hrs live hands-on sessions with industry expert

The live interactive sessions will be delivered through online webinars. All sessions are recorded. All instructors are full-time industry Architects with 14+ years of experience.

Real world Projects

You will be working on real case studies and solving real world problems.

Lifetime Access & 24x7 Support

Once you enroll for a batch, you are welcome to participate in any future batches free. If you have any doubts, our support team will assist you in clearing your technical doubts.

Weekly 1-on-1 meetings

If you opt for the Mentorship Track with Industry Expert, you will get 6 thirty minute one-on-one sessions with an experienced Data Scientist who will act as your mentor.

Benefits of Online Data Scientist Course

How will I benefit from the Mentorship Track with Industry Expert?

  • Learn by working on an end to end Data Science project approved by Industry Expert.
  • Meet every week, 1-on-1, with an experienced Data Scientist who will act as your mentor.
  • Highlight this globally recognized certificate in your resume and LinkedIn profile.
  • To take advantage of this opportunity, please check "Mentorship Track with Industry Expert" when you enroll.

How will this data science online course benefit me?

Prepare yourself for a career as a Data Analyst and Data Scientist.

  • Live online faculty led training
  • Learn NumPy - foundation library for Data Science in Python
  • Learn SciPy - key algorithms core to Python's scientific computing
  • Learn Pandas - library for data analysis and manipulation
  • Learn Matplotlib - python module for visualization to make graphs, pie charts
  • Learn SciKit - python module for machine learning

How will this online data science course help me get data analyst or data scientist jobs?

  • Display Project Experience in your interviews

    The most important interview question you will get asked is "What experience do you have?". Through the ProjectPro live classes, you will build projects, that have been carefully designed in partnership with companies.

  • Connect with recruiters

    The same companies that contribute projects to ProjectPro also recruit from us. You will build an online project portfolio, containing your code and video explaining your project. Our corporate partners will connect with you if your project and background suit them.

  • Stay updated in your Career

    Every few weeks there is a new technology release in Big Data. We organise weekly hackathons through which you can learn these new technologies by building projects. These projects get added to your portfolio and make you more desirable to companies.

What if I have any doubts?

For any doubt clearance, you can use:

  • Discussion Forum - Assistant faculty will respond within 24 hours
  • Phone call - Schedule a 30 minute phone call to clear your doubts
  • Skype - Schedule a face to face skype session to go over your doubts

Do you provide placements?

In the last module, ProjectPro faculty will assist you with:

  • Resume writing tip to showcase skills you have learnt in the course.
  • Mock interview practice and frequently asked interview questions.
  • Career guidance regarding hiring companies and open positions.

Data Science Course Curriculum

Module 1

Introduction to Python Programming

  • Introduction to Data Science
  • Introduction to Python
  • Basic Operations in Python
  • Variable Assignment
  • Functions: in-built functions, user defined functions
  • Condition: if, if-else, nested if-else, else-if
Module 2

Data Structure - Introduction

  • List: Different Data Types in a List, List in a List
  • Operations on a list: Slicing, Splicing, Sub-setting
  • Condition(true/false) on a List
  • Applying functions on a List
  • Dictionary: Index, Value
  • Operation on a Dictionary: Slicing, Splicing, Sub-setting
  • Condition(true/false) on a Dictionary
  • Applying functions on a Dictionary
  • Numpy Array: Data Types in an Array, Dimensions of an Array
  • Operations on Array: Slicing, Splicing, Sub-setting
  • Conditional(T/F) on an Array
  • Loops: For, While
  • Shorthand for For
  • Conditions in shorthand for For
Module 3

Basics of Statistics

  • Statistics & Plotting
  • Seabourn & Matplotlib - Introduction
  • Univariate Analysis on a Data
  • Plot the Data - Histogram plot
  • Find the distribution
  • Find mean, median and mode of the Data
  • Take multiple data with same mean but different sd, same mean and sd but different kurtosis: find mean, sd, plot
  • Multiple data with different distributions
  • Bootstrapping and sub-setting
  • Making samples from the Data
  • Making stratified samples - covered in bivariate analysis
  • Find the mean of sample
  • Central limit theorem
  • Plotting
  • Hypothesis testing + DOE
  • Bivariate analysis
  • Correlation
  • Scatter plots
  • Making stratified samples
  • Categorical variables
  • Class variable
Module 4

Use of Pandas

  • File I/O
  • Series: Data Types in series, Index
  • Data Frame
  • Series to Data Frame
  • Re-indexing
  • Operations on Data Frame: Slicing, Splicing (also Alternate), Sub-setting
  • Pandas
  • Stat operations on Data Frame
  • Reading from different sources
  • Missing data treatment
  • Merge, join
  • Options for look and feel of data frame
  • Writing to file
  • db operations
Module 5

Data Manipulation & Visualization

  • Data Aggregation, Filtering and Transforming
  • Lamda Functions
  • Apply, Group-by
  • Map, Filter and Reduce
  • Visualization
  • Matplotlib, pyplot
  • Seaborn
  • Scatter plot, histogram, density, heat-map, bar charts
Module 6

Linear Regression

  • Regression - Introduction
  • Linear Regression: Lasso, Ridge
  • Variable Selection
  • Forward & Backward Regression
Module 7

Logistic Regression

  • Logistic Regression: Lasso, Ridge
  • Naive Bayes
Module 8

Unsupervised Learning

  • Unsupervised Learning - Introduction
  • Distance Concepts
  • Classification
  • k nearest
  • Clustering
  • k means
  • Multidimensional Scaling
  • PCA
Module 9

Random Forest

  • Decision trees
  • Cart C4.5
  • Random Forest
  • Boosted Trees
  • Gradient Boosting
Module 10

SVM

  • SVM - Introduction
  • Hyper-plane
  • Hyper-plane to segregate to classes
  • Gamma

Data Science Projects

  • Walmart Store Sales Forecast using Linear Regression Models

    Project of modeling retail data is the need to make decisions based on limited history. If Christmas comes but once a year, so does the chance to see how strategic decisions impacted the bottom line.

    Project Image - 1

    In this project, you are provided with historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and you must project the sales for each department in each store. To add to the challenge, selected holiday markdown events are included in the dataset. These markdowns are known to affect sales, but it is challenging to predict which departments are affected and the extent of the impact.

  • Predict the Survival of passengers on Titanic using Logistic Regression

    The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.

    Project Image - 2

    One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.

    In this Project, you have to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply logistic regression to predict which passengers survived the tragedy.

  • Bike Sharing Demand Problem

    Bike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. Using these systems, people are able rent a bike from a one location and return it to a different place on an as-needed basis. Currently, there are over 500 bike-sharing programs around the world.

    Project Image - 3

    The data generated by these systems makes them attractive for researchers because the duration of travel, departure location, arrival location, and time elapsed is explicitly recorded. Bike sharing systems therefore function as a sensor network, which can be used for studying mobility in a city. In this project, you are asked to analyse and understand the cyclical and seasonal nature of bike usages also identify the key factors which affects bike usages. Also, calculate Density of Bike Demand, Key Drivers of Bike Demand and Daily and weekly pattern in the Bike Demand.

  • Clustering of MNIST Digit Image Data

    The data for this competition were taken from the MNIST dataset. The MNIST ("Modified National Institute of Standards and Technology") dataset is a classic within the Machine Learning community that has been extensively studied.

    Project Image - 4

    In this project, you have to identify how efficiently clustering works for MNIST image Data. The Data Contains the image pixel as feature. Also, identify which type of clustering works better for the Data. Find if clustering method able to cluster the data into 10 clusters. How efficiently is the clustering (calculated by how images of same digit are put in the same cluster).

  • Predict the Driver Alertness using Tree Based Classification Algorithm

    Driving while distracted, fatigued or drowsy may lead to accidents. Activities that divert the driver's attention from the road ahead, such as engaging in a conversation with other passengers in the car, making or receiving phone calls, sending or receiving text messages, eating while driving or events outside the car may cause driver distraction. Fatigue and drowsiness can result from driving long hours or from lack of sleep.

    Project Image - 5

    The objective of this project, to build a classification model for driver alertness using Driver information, Vehicle information and Environment variables. Using this model, predict the driver state. Also, find whether boosting gradient method works better than Random Forest.

Data Science Certifications

The Data science certifications offered by DeZyre prepares you to advance your career prospects by developing essential data science skills. Since Python and R are the most widely used data science programming languages today, DeZyre's data scientist certifications in Python and R will help you stand-out in the job market and get you closer to becoming a Data Scientist.

Python Certification for Data Science

DeZyre's Python online training for data scientists covers the fundamentals of data analytics and data science pipeline using python libraries such as Numpy, SciPy, SciKit etc. This course also covers essentials of statistics for data science in python. In this python online course for data science, you will solve real data science problems across multiple domains using python. Upon successful completion of the data science projects you will be awarded an online Data Science Certificate for Python.

R Programming Certification for Data Science

DeZyre's R programming online training and certification for data science will help make you an expert at understanding a data problem, designing the analysis and applying the right predictive modelling using R to glean valuable business insights. This R certification training for data science will help you master practical data science with R using statistical computing and machine learning through a series of data science projects. Upon successful completion of the data science projects you will be awarded an online Data Science Certificate for R.

Data Scientists Training Course Reviews

FAQ’s related to Data Science Online Courses

  • Do you provide any placement assistance for a data scientist job?

    As there is an increasing demand for the job role of a data scientist, we help data science certified students to build their individual data science project portfolio that will help them showcase their data science skills to prospective employers. We help our students prepare their data science resume, work on real-life data science projects, provide a set of data science interview questions and also provide guidance with data scientist job interview preparation.

    Disclaimer: We do not guarantee any kind of placements but if you complete the data science course and the projects attentively you will have a good hands-on working experience to land a top gig as a data scientist in any company.

  • What is Data Science?

    Big data is one of the industry’s biggest buzzwords and the other one growing with it is the term Data Science. Data science is at its exponential uptake today and is expected to power the future. Business are producing data at a rapid pace which exceeds the capacity to extract value from it. Data is the strongest strait of any business today. The need for making smarter and faster data-driven decisions is increasing exponentially. Data science is emerging as a hot new field as businesses emphasize on using all the available and relevant data effectively. Data Science is a multi-disciplinary field to study how information or data can be turned into a valuable resource for implementing various business and IT strategies.

    Data science is a hot technology nowadays amongst businesses as it helps them discover novel marketing opportunities, increase efficiencies, rein in costs and gain competitive advantage by coupling computer science with a highly mature discipline like statistics. The main goal of data science is to build robust decision making capabilities around evidence based analytical rigor. Data science enables the creation of data products that acquire value from the data.


    Data science discipline involves using statistical techniques, mathematics and algorithmic design techniques to find solutions to complex analytical business problems. It is a deep knowledge discovery using data explorations and data inference.

  • What are the prerequisites to join a Data Science training course?

    An advanced understanding of Mathematics and Statistics concepts, basic programming like C, C++, Java, Python or R will be a big plus. Knowing how to write basic SQL queries will help you advance quickly in your data science career. A PhD, knowledge of Hadoop or other distributed processing systems is not absolutely necessary, but many companies are asking for Apache Spark as a skill for a data scientist job role. You can check out this blog post for a more detailed discussion on prerequisites to learn data science 

  • Why should I learn Data Science from ProjectPro instead of other providers?

    The Data Science course curriculum at ProjectPro, has been developed in partnership with Industry Experts, having 9+ years of experience in the field - to ensure that the latest and most relevant topics are covered. Our curriculum is also updated on a monthly basis. This is the only Data Science learning experience where you start coding immediately in the first class. We do not waste any time on slides and theory. Once you complete the project, we will issue the certificate based on your performance.

    Data Science Online Training with ProjectPro aims at moulding students or professionals who want to make big as enterprise data scientists. ProjectPro helps students learn data science from industry experts by encapsulating lot of projects in Python and R to provide experiential learning. ProjectPro’s data science in Python and data science in R course helps you learn by working on ProjectPro approved projects that aim at analysing large datasets.

    The hands-on experience in Python and R helps students build a strong portfolio in Python and R language gaining traction from the hiring managers of well-established companies. As a part of ProjectPro’s Data Science Online Training we emphasize on teaching the most beginner-friendly languages Python and R because they are the workhorse of a data scientist-Python and R are used for developing most of the big data applications and are an integral part of production data science work. Close mentoring with industry experts, best-in-class data science course curriculum, lifetime course access, 24x7 support and personalized instructions from the mentor make data science online training with ProjectPro a supreme choice for people who want to start a career in data science.

    If you dream of a data science career full of admiration, accomplishments and with a huge pay package at the end of the month then the ProjectPro certification offered at the completion of Data Science training will add a feather to your cap by landing you a top gig as a Data Scientist or Data Analyst.

  • How will the Data Science training at ProjectPro be conducted?


    The Data Science training at ProjectPro will be conducted through virtual classrooms. There will be 45 hours of live interactive online webinars with the faculty. You will also be working on practical assignments throughout the duration of the course. At the end of the course, you will need to submit a final project. 

  • What kind of lab and project exposure do I get?

    The entire course is a lab. You are only coding 100% of the time. We do not waste your time with slides and theory. From the first minute to the last minute of the class you are working on hands-on projects. 

  • What are the benefits of taking up the Data Science Course at ProjectPro?

    With big data becoming the life blood of business, data analysts and data scientists with expertise in Hadoop, NoSQL, and Python and R language are hard to come by. Students or professionals who want an extra edge for their next big data job or are angling for a promotion-ProjectPro Certification offered at the completion of Python and R course is a third-party proof of skills that provides added advantage. If you are a recent graduate or someone looking to break into data science from a different fields then ProjectPro Certified Data science courses in Python and R are likely to suit your needs.

    Data Science ProjectPro Certification proves to employers that an individual has the right skillset required for the data scientist or data analyst role as it measures the knowledge and skills against industry and vendor specific benchmarks. ProjectPro Certification provides a flexible, low-risk way to explore data science career.

  • What are the objectives of this Data Science Course ?
    • This Data Science Course is designed by closely working data scientists and analysts at leading technology companies.
    • This course provides the skills and expertise required to become a data scientist and also helps data analysts broaden their skillset.

    • Students can learn top data science programming languages like Python and R from industry experts to deliver new business insights and competitive intelligence.

    • On completing this data scientist course , students will gain expertise in core skill areas of a data scientist role like data manipulation, data visualization, data exploration and various statistical techniques.

    • Master various data analysis techniques to discover new relationships, patterns or trends in large complex data sets.

    • Learn to communicate the results of data analysis and findings through various data visualization techniques.

    • Helpful career guidance on completion of the course to prepare students for rewarding employment as a Data Scientist or Data Analyst at well-established companies.

  • Who will be my faculty?

    The faculty at ProjectPro are all experienced Data Scientists with more than 14+ years of experience in the Industry. All our faculty are working professionals. All your instructors will be industry practitioners of Python / Data Science. They have all been approved to teach Data Science at ProjectPro, after going through a series of stringent tests. So you can be assured that whatever you are learning is cutting edge and industry relevant.

  • What are the career prospects of a Data Scientist?

    Data Scientists are some of the most sought after professionals in the world of big data analysis. Companies are pulling all stops to efficiently analyze the data that their business is generating. Every company, government program or institution that uses data are looking to hire data scientists. At any given point of time, job portals have over 100,000 data science open positions worldwide.

  • What is Data Scientist job Role?

    Data Science has emerged with a sexy labelled profession Data Scientist who make sense of huge amounts of big data by doing data science. Data scientist makes data science sing by mastering math, computer programming in Python, R, Hadoop, etc. and statistics to derive insights using the same level of business understanding and gut instinct that drive company executive decisions. Data Scientist is a high ranking professional who has intense curiosity to make discoveries in the world of big data using technologies like Hadoop, Python, R, NoSQL that make taming big data possible for businesses.

    Data scientist transform huge amounts of formless data into structured format for making big data analysis possible. A data scientist identifies rich data sources, merges them with other incomplete data sources and cleans the resulting set. Data scientists are the go-to professionals that help business decision makers shift from ad hoc analysis to an unending conversation with data. They are powerful and hybrid rare breed of data hackers, data analysts, communicators and trusted advisors.

    As the title implies, a data scientist requires broad set of hard and soft skills as they are unicorns. The 3 main competencies a data scientist must possess are Business Acumen, Technology and Hacking Skills, and Mathematics expertise. An enterprise data scientist should possess emotional intelligence along with education and experience in big data analytics.

    Data scientists are highly sought after professionals by many startups in the bay area and also well-established companies like Google, Facebook, LinkedIn, Pinterest, Accenture, etc. The supply of big data professionals who can effectively turn raw data into business insights using various tools and technologies like Hadoop, Python, NoSQL, Machine Learning, and Statistical Analysis is limited. The data science skills gap signifies that many people are learning or trying to learn data science.

     

  • What if I miss a data science training session?

    After the particular data science class is completed, all ProjectPro students are provided with the recordings of the class. If by any chance, a student misses any of the sessions he/she can go through the data science class recordings from the LMS dashboard before the next data science class. If there is any other simultaneous data science training batch going on, they can attend that as well to prepare themselves before the next class with the data science concepts they have missed in the previous class.

  • Who provides the data science certification?

    On completing the data science course, data science projects submitted by students are evaluated by the industry experts based on which a data science certificate is awarded to the students from ProjectPro. The data science certificate mentions that you are a certified data scientist with Python programming or a certified data scientist with R programming or both depending on the data science trainings you complete with ProjectPro.

  • What previous experience do I need to have to take this data science training course?

    Basic knowledge in quantitative discipline along with fundamentals of mathematics, statistics, probability and linear algebra is recommended. However, for professionals who do not have fundamental knowledge of these subject areas, ProjectPro provides some basic introductory learning videos on Probability and Statistics that will prepare you for this data science course.

  • Who should learn Data Science?

    Everybody cannot become a data scientist, if they could there would not be shortage of data science skills and premium salaries for data scientists. Anyone who has a flair for number crunching, love for data, storytelling skills, logical reasoning abilities, programming expertise and problem solving attitude can learn data science if approaching with a right frame of mind.

    Professionals in different job functions or industries who want to help their company leverage big data should learn data science. Apart from students ,other professional who can benefit by learning data science are database administrators, business analysts , Statisticians, researchers, computer scientists and data engineers.

    The biggest myth revolving around data scientist career is that people having a Master’s or Ph.D. degree in Computer Science or Quantitative Computing only can learn data science. The increasing costs, changing demand and the Internet have disrupted the traditional path of learning data science. Whether it is person with a Bachelor’s degree in statistics or computers or a person with minimal programming background can learn data science technologies like Python and R in a structured eLearning environment at an affordable price when compared to a Master’s degree.

  • Why should I take up Data Science training?

    A Data Scientist has to be skilled in various fields, methods and technologies. A comprehensive training on data science, will help you get started on updating your skills for a Data Scientist career. Learning from Industry experts will give you an idea on what a Data Scientist needs to achieve and how to build strategies keeping in mind the business end goals. 

    Reasons to enrol for ProjectPro's Data Science Training-

    1) You want to gain specialization in Data Science

    2) You are just starting out your career in data science.

    3) You want to advance in your current job role.

    4) You want to switch careers.

  • What is the Difference between Data Science with R and Python?

    Python and R are two good open source choices for programming in pursuit of robust data science. Python language for data science is a general purpose programming language whereas R language for data science is developed with statisticians in mind. Python and R complete each other gracefully and are equally worth for traditional statistical analysis tasks as they inter-operate with each other. A data scientist must know both Python and R language so that they can leverage the strengths of these languages avoiding their weaknesses based on the kind of data problem.

  • Why should I learn Python for a Data Science career?

    Data Science is an emerging and extremely popular function in companies. Since the volume of data generated has increased significantly a new array of tools and techniques are deployed to make decisions out of raw big data. Python is among the most popular tools used by Data Analysts and Data Scientists. It's a very powerful programming language that has custom libraries for Data Science.

Data Science Tutorials

  • What is a Python List Data Structure?

    List is one of the most versatile data structures in Python programming language that stores an ordered collection of items. The simplest way to understand this is to just imagine it to be like your monthly grocery shopping list , except that in your monthly grocery shopping list the items usually are listed in a separate line whereas in Python programming language we separate the items in the list using a comma (,). List data structures in python are mutable and the value of an element in the list can be changed. A list is always enclosed in brackets and the elements in a list can be accessed using its index which begins from 0.

    Refer to this Python List Tutorial for in-depth understanding of List data strcuture concept in Python - Python Tutorial on List Data Structure

  • What is an index in pandas dataframe?

    Index in pandas dataframe is used to iterate through the data present in the dataframe.Pandas dataframe has a default index of 0. Suppose you have a file named "Sample.csv" containing the following data-

    0   CourseName  Cost

    1   Hadoop           399

    2   DataScience    699

    3   Spark              399

    Example Demonstrating the Use of Index in Pandas.Dataframe

    df2 = pd.read_csv("Sample.csv") #This step will read the CSV file into the Pandas Dataframe.

    for i in df2.index:

    print df2.CourseName.ix[i]

    print df2.Cost.ix[i]

    Output :

    Hadoop

    399

    DataScience

    699

    Spark

    399

  • How SVM can be used for Reggression and Classification?

    Support Vector Machines (SVM) use decision planes to make classification boundaries. A decision plane separates between a group of data points that belongs to different class association. A linear classifier separates the data points into their respective class groups with a one-dimensional line. Usually decision planes are complex structure that makes optimal classification of set of objects. Hyperplane classifiers are used for the classification task of different class objects, where multiples lines would be required for optimal classification.

    Support Vector Machines are required to perform such classification. In SVM, the original data points are transformed using kernel functions; such that the resulting class of data set can be classified using a linear classifier instead of a complex curve. Support Vector Machine (SVM) is primarily a classier method that performs classification tasks by constructing hyperplanes in a multidimensional space that separates cases of different class labels. SVM supports both regression and classification tasks and can handle multiple continuous and categorical variables.

  • What are some useful Data Analysis tools?

    Among many open source and free tools available on internet for Data Analysis, following are found to be most useful and important:

    1. CSVKit-- It has a host of Unix-like command-line tools for importing, analyzing and reformatting comma-separated data files.
    2. DataTables-- This jQuery plug-in creates sortable, searchable HTML tables from a variety of data sources; for example, an existing, static HTML table, a JavaScriptarray, JSON or server-side SQL.
    3. Highcharts JS-- It is a JavaScript library which provides an easy way to create professional-looking interactive charts for the Web.
    4. PowerPivot-- It is a Microsoft Excel Plugin which is used to handle big data sets more efficiently compared to the basic version of Excel.
    5. Weave-- It a visualization platform allows creation of interactive dashboards with multiple, related visualizations -- for example, a bar chart, scatter plot and map.
    6. Import.io-- It is used for data extraction through web sources. It requires an input for parameters and generates data which can be exported for analysis.

  • How to find key trees/features from a trained random forest?

    After training a random forest, the trees are stored in the estimator_ attribute. In order to extract a key_tree, first define its characteristics and features. Based on that individuals trees would be ranked and then can be sorted for further use.

    The command forest.feature_importances_ can also be used to find the trees/features from a trained random forest. This command sorts input features based on their relative importance.

  • How to select features for random forest using varImp function?

    Caret's train() function produce model equation that use selective features. Train() function create these models and they have a built-in feature selection. Predictor() method can be called upon these models to return a vector which contains the predictors/features used in the final model. Built-in feature selection typically couples the predictor search algorithm with the parameter estimation and are usually optimized with a single objective function.

  • Natural Language Processing, Python Vs. R?

    Both R and Python offer open source toolkit that assists in Natural Language Processing. The prominent difference both the languages is that R is used for analytics and Python can be used for application development along with Language Processing. Also, R has some limitations in terms of memory. For instance, R holds all data in active workspace in RAM. That means, that while running R on  32-bit system, we have a upper limit of 4 GB RAM for R to access. Python offer much more flexibility in that aspect but it lacks the wide discipline of tool kits available for R.

  • What are the packages available for Natural Language Processing?

    There are various open source packages available online for Natural Language Processing depending upon the processing language and framework being used. CRAN task view aggregates R language packages that supports computational linguistic application like speech analysis, language analysis based on words, syntax, semantics and pragmatics.

    • tm is a text mining package within R.
    • OpenNLP is a collection of natural language processing tools, which perform application like tokenizer, sentence detector, syntactic parser and pos-tagger.
    • RWeka is an interface to Weka. It provides an aggregation of machine learning algorithms for data mining and its written in Java.
    • Natural Language Toolkit (NTLK) is a framework to create Python based application which can interact with natural human language.
    • Standford CoreNLP is a set of Natural Language Analysis frameworks and tools, that can process language text input and analyze it using Named Entity Recognizer (NER), Part of Speech (POS) tagger, sentiment analysis and other NLP tools.
  • What is Gibbs Sampling Strategy?

    Gibbs Sampling is a Monte Carlo Markov Chain model for acquiring a series of sample values that are approximated using a pre-defined multi-variable probability distribution function (joint pdf). Gibbs Sampling technique is mostly used Bayesian Inference. The marginal distribution of any subset of variables can be approximated by simply considering the samples for that subset of variables, ignoring the rest.

  • What is Bayesian Model or Bayesian Network?

    Bayesian model is a directed acyclic graph which depicts the conditional probabilities and dependencies of a set of random variable. The nodes in the directed acyclic graphs represents random variables which can be unknown parameters or latent variables or some observable quantities; whereas the edges in the Bayesian network/model represents conditional dependencies between these variables.

    If any two nodes in the Bayesian model are not connected, it implies that they are conditionally independent of each other. Algorithms are applied on Bayesian models to obtain model learning and inferring correlation between random variables.

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Data Science News

Annual Survey On Data Science Recruitment In India 2018.AnalyticsIndiaMag.com,June 29, 2018


MNC’s and Indian companies are on the verge of hiring data science experts who can help them garner insights from big data.A survey was conducted on 3 groups of people -job seekers, students and recruiting managers to find out the variations in skill sets, work experience and educational qualifications to get a complete idea of the hiring scenario in the data science industry. Here are some interesting insights that are real eye-openers for people looking for a data science job - i) 33% of the respondents to the survey mentioned that formal education is essential to get a data science job considering the current trends in the industry. ii) 48% of the respondents mentioned that some kind of a prior programming experience is important to get a data science job. iii) 35.8% of respondents said that some kind of a job experience is important to land a top gig in the data science sector. iv) 23.2% of the respondents said that it is difficult but not impossible to transition from a non-datascience background to a new tech like data science. v) 42.7% consider internship as the best way to land an entry-level data science job. vi) 47% said that knowing Python programming language is necessary to get a data science job with 39% respondents considering R programming language as the next best favorite for data science. vii) The three important skills necessary to flourish in data-science are -Statistical Modelling (35%), Machine Learning (28%) and Business Intelligence (17%). (Source- https://analyticsindiamag.com/annual-survey-on-data-science-recruitment-in-india-2018/ )

Understanding Healthcare's New Life Savers, Data Scientists.HCAnews.com, June 28, 2018


For a patient struggling with life in ICU, every second counts and the decision a healthcare professional makes can have a life or death consequence.Healthcare organizations are leveraging analytics to transform clinical and epidemiological data into actionable insights that can save and enhance a patient’s life which would otherwise not have been possible. One such startup is CLEW Medical that uses big data and machine learning for predictive analytics which guide patient care in the Intensive Care Unit.With the use of CLEW’s platform, physicians can now use real-world clinical data to find out what might work best for a patient in ICU rather than depending on any gut instinct , defensive medication or personal experience. (Source - https://www.hcanews.com/news/understanding-healthcares-new-life-savers-data-scientists )

Four Simple Steps To Get Your Organization Started With Data Science.Forbes.com, June 27, 2018


Here are the steps that Suneel Chakravorty, Co-Founder and Partner at Simple Fractal wants you to follow to begin your way into data science- i) Choose the right Dataset - Regardless of the nature of your organization (whether you are into insurance or manufacturing or any other business domain) , you must first identify the dataset that will help you advance the business. Doing so will give a healthy focus to your data science mission. ii) Set an initial goal - Decide what is the first thing you want to do with data. Set a goal that has achievable milestone and can render business value. iii) Skill up your Team with the Required Tools (SQL, Python, R, and more) based on the Requirements Your team should learn to use the tools and also have basic intuition behind models to ensure that they are proficient enough to build useful data products. iv) Hack on the Data - Play with the data to derive initial results and keep iterating the process. (Source - https://www.forbes.com/sites/forbesnycouncil/2018/06/27/four-simple-steps-to-get-your-organization-started-with-data-science/#3ae312546d15 )

Data scientists are in-demand and well paid - so why is there a skills gap? Computing.co.uk, June 18, 2018.


According to HBR, data scientist is the most sexiest job of 21st century with high demand . IBM forecasts suggest that the number of data scientists required will increase by 28% by 2020 with 2.7 million open job roles for data science professionals in US alone. Still, these job roles are the most difficult to fill due to the skills gap and require on average minimum of 45 days to fill. According to Kristin Rahn, Director of Product Management for Data Science and Analytics there is a shortage of people for all STEM roles and students find analytics classes most disinteresting to attend. Another reason that Krisitn highlights for the skills gap is that data science is performed by business for the benefit of others which means that data science professionals are required to have communication and consulting skills. There are professionals who have the technical knowledge but they lack in communication and consulting which are required to become valuable in a commercial environment. (Source - https://www.computing.co.uk/ctg/opinion/3034263/data-scientists-are-in-demand-and-well-paid-so-why-is-there-a-skills-gap )

Why it's your fault your data scientists keep quitting?TechRepublic,June 8, 2018


Data scientist is a highly paid job role but poorly understood which is the sole reason why good data scientists keep quitting organizations. Many companies start hiring data scientists without even having a suitable infrastructure in place to get the most out of AI which leads to cold start problem.Moreover, many companies tend to hire experienced and senior data science professionals rather than hiring juniors leading to unhappy relationship between them and the employer.The job role of a data scientist is to write smart machine learning algorithms to derive valuable insights but he/she cannot do so because they are stuck up in sorting out the data infrastructure and creating analytic reports. This leads to frustration for the employers and disappointment to the data scientist making them quit the job. (Source - https://www.techrepublic.com/article/why-its-your-fault-your-data-scientists-keep-quitting/ )

Data Scientists Training Jobs

Data Scientist II

Company Name: Zurich
Location: Schaumburg, US-IL
Date Posted: 24th May, 2018
Description:

In particular, the Data Scientist II:

  • Partners with business stakeholders to translate business objectives into clearly defined analytical projects
  • Conducts exploratory data analysis, model development, model monitoring, and benefit estimation
  • Works with the business to develop and implement a strategy on how to use the predictive model for most impact
  • Establishes and maintains collaborative relationships throughout the organization
  • Demonstrates commitment to continuous education and personal development on areas ...

Lead Data Scientist

Company Name: Nielsen
Location: Chicago, Illinois
Date Posted: 23rd May, 2018
Description:

Key Responsibilities

  • Lead detailed analysis to support product enhancements and key initiatives
  • Manage multiple projects simultaneously
  • Represent Product Enhancement in meetings and in interactions with other departments
  • Oversee end to end implementation of product solutions
  • Ability to evaluate current methodologies quickly to identify opportunities for enhancement
  • Present findings and recommendations on methodology
  • Document findings, methodologies, and best practices
  • Detect, troubleshoot, and resolve system anomalies ...

Data Scientist

Company Name: Tempe, AZ
Location: Northern Trust
Date Posted: 14th May, 2018
Description:

Job Role and Responsibilities

This role will work on the Infrastructure Data Analytics Team to create solutions which increase stability, create clarity, and reduce the operational costs of highly visible applications and software systems for Northern Trust. These solutions require the use of Big Data tools such as Splunk, ELK, and Hadoop, as well as integration with tools such as Service-Now.