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.
The most important interview question you will get asked is "What experience do you have?". Through the DeZyre live classes, you will build projects, that have been carefully designed in partnership with companies.
The same companies that contribute projects to DeZyre 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.
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.
For any doubt clearance, you can use:
In the last module, DeZyre 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 DeZyre, 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 DeZyre aims at moulding students or professionals who want to make big as enterprise data scientists. DeZyre helps students learn data science from industry experts by encapsulating lot of projects in Python and R to provide experiential learning. DeZyre’s data science in Python and data science in R course helps you learn by working on DeZyre 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 DeZyre’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 DeZyre 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 DeZyre 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 DeZyre 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-DeZyre 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 DeZyre Certified Data science courses in Python and R are likely to suit your needs.
Data Science DeZyre 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. DeZyre 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 DeZyre 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 DeZyre, 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 DeZyre 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 DeZyre. 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 DeZyre.
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, DeZyre 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 DeZyre'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.
When creating a dataframe using the Python pandas data science library there is an option to add input to the ‘index argument’ so that developers can have the desired index they want. If the index argument is not specified then the index for the dataframe begins with 0 and continues until the last row of the dataframe is encountered. Even though the index is specified automatically, developers can make one of the columns as the index by using the set_index () function.
Tuples in Python are immutable and the value cannot be deleted, edited or added. Trying to delete the first element from the tuple will results in an error as shown below -
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'tuple' object doesn't support item deletion
Python Dictionaries are important data structures in programming used to associate or map items you want to store to keys you need to retrieve them. Just relate to dictionary data structure in Python to the address book where you have to find the address of a person by knowing their name. Here we associate keys (name) with values (address or contact information).Key in a python dictionary must be unique because if you have two people with the exact same name then you will not be able to find the correct information. The term dictionary is used for these data structures as they work exactly like an actual dictionary full of words. Python Dictionary data structures map keys of any immutable type like tuples, strings or numbers to heterogeneous values.
Consider any language, every word maps to a meaning, just like that in a python dictionary we map keys (words) to values (meanings). Python Dictionary data structures can be referred to like lookup tables where you one value to lookup another value. Every key in a python dictionary is separated from its value using a colon (:) symbol. A dictionary data structure in Python is enclosed in curly braces and the items in the dictionary are separated by commas, just like other data structures.
Python Dictionary Example:
dict = {'Name': ‘DeZyre’, ‘Training’: ‘Hadoop’, 'Class': ‘Best’}
len () function is used to get the length of a Tuple Data Structure in Python.
Example:
DeZyre_DataScience_Tuple = (“Hadoop”,”Spark”, “Python”,”R Programming”, “NoSQL”)
print (“Number of Trainings Offered by DeZyre is: len (DeZyre_DataScience_Tuple));
Number of Trainings Offered by DeZyre is: 5
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
A simple tuple data structure in Python can be created as shown below -
DeZyre_DataScience_Tuple = (“Hadoop”,”Spark”, “Python”,”R Programming”, “NoSQL”)
You can create an empty tuple data structure in Python using empty parenthesis as shown below –
Sample_tup= ();
Even if there is a single value for the tuple, comma should be included after the value. A tuple in Python with a single value can be created as shown below –
Sample_Tup= (“DeZyre”,);
On executing the above python list append function both the values will be appended at the end of the list as a single element only. When using append for python lists, regardless of whether you have a single element or multiple elements- it will be added as a single element. To confirm this you can just try to print the length of the DeZyre_DataScience_List after executing the python list append function as shown above.
Print (“Number of Elements in the List is: len (DeZyre_DataScience_List));
Output:
Number of Elements in the List is: 7
Tuples are similar to list data structures that hold multiple objects but are immutable unlike lists. Tuples are generally used when a UDF or a statement assumes that the collection of values in the tuple will not change. As tuples are immutable(one cannot add, delete or edit any value inside the tuple) they consume less memory and are faster at data processing when compared to list data structures. Tuples are immutable but they can hold data that is mutable. Tuples are created by separating the item values through a comma and an optional parenthesis at the start and end of a tuple. Parenthesis are optional when creating a tuple but it is always good to use parenthesis to define the start and end of a tuple to ensure that nested tuples are processed correctly.
The output for the above code will return 60 because negative index accesses the list from the end. Similarly, if you try to access DataScience_List [-2], it will display the second last element from the list.
DeZyre_DataScience_List = [1, 20, 3, 40, 5, 60]
To access the first element of the list data structure use the below line of python code –
DeZyre_DataScience_List [0];
Output on executing the above line will return the first element from the list i.e. 1.
There are several methods that can be applied to a list data structure in Python append (), sort (), reverse (), extend (), insert (), remove (), pop ().
In particular, the Data Scientist II:
Key Responsibilities
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.