Build an online data science project portfolio with your project code and video explaining your data science project. This is shared with recruiters.
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.
You will be working on real case studies and solving real world problems.
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.
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.
For any doubt clearance, you can use:
In the last module, ProjectPro faculty will assist you with:
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.
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.
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.
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 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.
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.
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.
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).
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
len () function is used to get the length of a Tuple Data Structure in Python.
ProjectPro_DataScience_Tuple = (“Hadoop”,”Spark”, “Python”,”R Programming”, “NoSQL”)
print (“Number of Trainings Offered by ProjectPro is: len (ProjectPro_DataScience_Tuple));
Number of Trainings Offered by ProjectPro is: 5
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.
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.
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.
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.
Data Type is a classification for identifying types of data that determines the value of that type, that operations that can be performed on the that type, the meaning of that data and the kind of values that can be stored in that type.
Below mentioned are the primary data types that are being used by majority of programming languages:
4. Floating-point number
5. Alphanumeric Strings
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.
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.
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.
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.
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.
In particular, the Data Scientist II:
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.