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Home / Data Science & Deep Learning / Practical Data Scientist Online Program
"With hundreds of companies hiring for the role of Data Scientist, 12 million new jobs will be created in the field of Data Science by the year 2026." - (Source). Data is Multiplying at an astonishing rate and we have more and more data coming in all the time. Data is collected to improve decisions about some aspect of business, government, and society. Data Science turns this data into valuable insights through quantitative analysis and powers business value. A few years ago, if a person had the knowledge of various algorithms and how the algorithms work, that would have been sufficient to get a job as a data scientist. But, as the market has matured, hiring managers and companies across the domains are focusing on bringing data scientists with knowledge of delivering models in production. A certification in Practical Data Science will open doors to unlimited opportunities making you the modern superhero who can tease actionable insights out of gigabytes of data.
Total Duration
4 Months
Prerequisites
Build Data Pipeline and Data Architecture in alignment with Business Objectives Deploying Model on Cloud with Auto ML for auto up-gradation of models. Deploying Model in Distributed Environment using Big Data and Develop an end to end product through the Front end, Middleware & Back end systems. Practical Data Science Program focuses more on developing an end to end data science solution on one of the cloud environments (AWS, GCP, Azure, etc.) as well as on-premise systems. The data science market has matured over the past few years where the focus was on algorithm development and research but is now slowly maturing into delivering data science solutions that are production-ready. Learn how to build data science products at scale leveraging distributed computing capabilities. This course aims to create data scientists with a set of skills that would accomplish that goal and deliver production-ready models. Additionally, also learn how to strike a balance between business objectives, performance, and accuracy. As such it will be an interdisciplinary course that varies from learning about algorithms, model development, software engineering, version control, and continuous integration/ continuous development (CI/CD) pipelines. What is Practical Data Science? Data Science is the learning and practice of extracting meaningful information, knowledge, and insight from huge amounts of data and providing for better decision making and problem-solving techniques. It includes having expertise in computation, statistics, analytics, data mining, data modeling, data visualization, and programming. A data scientist facilitates collecting, compiling, interpreting, modeling, formatting, manipulating, and drawing predictions from massive amounts of data.
This Practical Data Science course aims to provide a practical introduction to data science analysis which includes the collection of data and its visualization and presentation along with statistical model building using machine learning and using various techniques to scale these methods. This course would also include using a variety of machine learning methods like linear and non-linear regression, classification, unsupervised learning, boosting, clustering, neural nets, and deep learning, etc. As the name suggests students will be exposed to the practical aspects of data science using the above techniques. Students will also explore to diagnose problems with data science pipelines and also delve into the critical issue of converting business problem statements into data problems. Students will be able to perform independent statistical data analysis on real data sets and develop skills to query common data stores using SQL, Python Pandas, Hadoop, and Spark. Join this Practical Data Science course which will demonstrate your capabilities and potential of being a complete professional in the field of Data Science with comprehensive knowledge of its fundamentals. You will also
Block Your Time
130 hours
Classroom Sessions
140 hours
Assignments
2 Live Projects
Who Should Sign Up?
This module on Practical Data Science is designed to achieve practical results in Data Science. This is where you will learn to visualize, analyze, and model data. This training will equip you with the most in-demand career skills from various industries like banking, healthcare, and tech startups. The modules introduce you to Data Science, Machine Learning, Statistics, Analytics, Python, and help you develop skills that are needed to demystify the data around you. You will also be able to demonstrate an understanding of the core concepts of analytics and automation. You will be able to create sophisticated statistical models using advanced skills in Python, Data Analysis, and Machine Learning. So, don’t wait too long to add Data Science credentials and join this course in Practical Data Science and let your tech career power the greatest technologies today.
The goal of this module is to introduce the basic framework of data science called Cross Industry Standard Process for Data Mining (CRISP-DM). Learners will understand the philosophy behind the data science framework. In addition to that, this module will also delve into the critical issue of converting business problem statements into data problems.
This module introduces some of the modern tools and techniques of software development such as version control using Git. It would also be helpful to have an understanding of the Agile processes in a Data Science project.
In this module gain immense knowledge of Cloud Computing and disadvantages of on-premise infrastructure, Deploying a Machine Learning Model end to end on the cloud using Amazon Web Services like Cloud Formation, Lambda, S3 and Machine Learning Services used in various Projects.
One need to understand cloud computing and essential concepts of Cloud Computing - Cloud Deployment Models, Cloud Service Models. Get an overview of AWS Global Infrastructure, Regions and Availability Zones
Understanding the security features of AWS through IAM service through Users, Groups Roles and Policies
Learn how to use storage services in AWS through S3, Creation of a Bucket, Advantages of S3, Properties of S3, Storage Classes. Connecting S3 to another AWS service, Building a Data Lake using S3.
Gain Broad idea about Machine Learning on Cloud, Sage Maker as a Service, Various sub services under Sage Maker, Creation of Sage Maker Notebook Instance, working with Jupyter Notebook instance, Overview of Sage Maker Studio, Building a Model in Jupyter, Deployment of the Final Model
To Learn collect process and stream large streaming data, Firehose which helps to deliver the data into S3
Deploy Quickly your required AWS services using Cloud Formation
Understanding API and applicability of the Amazon API Gateway Service to deal with Creating, Maintaining, Monitoring and Securing APIs.
SQL, NoSQL, NewSQL, Cloud Storage This module introduces the databases that typically exist in the business environment. Traditional (Structured data) databases like Oracle, MySQL, SQL Server, DB2, etc, and the query language (SQL). We also get used to the NoSQL databases (Unstructured data) like HBase, MongodB, Cassandra, CouchDB, etc. Understand the architecture and design of the new age databases capable of handling new age data requirements based on consistency, availability and partition tolerance.
This module will get the users up to speed on the programming requirements of being a Data Scientist. Python is emerging as the language of choice for Data Scientists but interested candidates can also choose to opt for R language. In the Python programming track, Object oriented programming concepts are introduced as well.
This module begins to set up the groundwork for the core skills of being a Data Scientist by introducing the learning to basic statistics. We will discuss probability distributions, descriptive and inferential statistics.
After gaining a basic introduction to statistics, this module will introduce Hypothesis testing and Analysis of Variance (ANOVA) and other useful statistical concepts.
This module is one of the most interesting, laborious and creative parts of the total model development process. It deals with understanding the data, visualizing it to find correlations and begins the process of getting the data ready for use by various machine learning algorithms.
This module is an important part of the data science lifecycle because it determines how features can be extracted from the dataset to maximize the output from machine learning algorithms.
This module introduces the popular machine learning algorithms that are used by data scientists for model development. Since this is a vast subject, we focus on just using a few examples of each paradigm of machine learning (supervised, unsupervised etc)
This module is a course in and of itself, but for the purposes of this course, we will review at a high level some of the most popular deep learning frameworks using Tensorflow, Keras and PyTorch.
Finally, we develop and evaluate a model. This will usually be an iterative process, where multiple models are developed and tested for effectiveness. Model evaluation techniques are introduced and the best practices are outlined.
This module describes the CI/CD pipeline to deploy models in the cloud environment using Jenkins (AWS/GCP).
AWS:
Brief introduction to
Using all the above services, build an end to end machine learning pipeline that runs in a fully managed production environment.
Finally, this module wraps up the course by describing the best practices on how to effectively monitor the models in production and when to retrain them.
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Data Science technologies paramount in the effort to collect, prepare, predict, and respond to the proactive and accelerated growth of data. The trends that will dominate the data and analytics market to prepare for a reset will include smarter and faster integration of AI technologies from piloting to operationalizing phase. It is predicted that 77% of enterprises will engage in more responsible AI that will contribute to an epic increase in streaming data and analytics infrastructures. The other trend to look out is Augmented data management, uses AI and ML technologies to optimize and improve operations, configuration, security, and performance. It also converts metadata to powering dynamic systems and facilitates automation in redundant data management tasks.
By the year 2022, 85% of data and analytics innovation will exploit cloud capabilities to improve the workload’s performance and for cost optimization. Next comes in a technology that provides transparency for complex networks of participants and provides the full lineage of assets and transactions and that is the Blockchain Technology. The other new trend is the graph technologies and algorithms that will be used to comb through thousands of data documents to uncover hidden patterns and relationships. The application of Graph Analytics ranges from discovering new possible treatments for diseases that often have negative outcomes for patients, traffic route optimization n, fraud detection, and social network analysis to genome research. Nothing else adds a bigger opportunity to the employability of professionals for the Data Science industry that needs 6 million workers every year. Get ready to enroll for the practical Data Science training in the USA if you want to power your dreams ahead.
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Earn a certificate and demonstrate your commitment to the profession. Use it to distinguish yourself in the job market, get recognised at the workplace and boost your confidence. The Practical Data Scientist Certificate is your passport to an accelerated career path.
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Alumni Speak
"The training was organised properly, and our instructor was extremely conceptually sound. I enjoyed the interview preparation, and 360DigiTMG is to credit for my successful placement.”
Pavan Satya
Senior Software Engineer
"Although data sciences is a complex field, the course made it seem quite straightforward to me. This course's readings and tests were fantastic. This teacher was really beneficial. This university offers a wealth of information."
Chetan Reddy
Data Scientist
"The course's material and infrastructure are reliable. The majority of the time, they keep an eye on us. They actually assisted me in getting a job. I appreciated their help with placement. Excellent institution.”
Santosh Kumar
Business Intelligence Analyst
"Numerous advantages of the course. Thank you especially to my mentors. It feels wonderful to finally get to work.”
Kadar Nagole
"Excellent team and a good atmosphere. They truly did lead the way for me right away. My mentors are wonderful. The training materials are top-notch.”
Gowtham R
Data Engineer
"The instructors improved the sessions' interactivity and communicated well. The course has been fantastic.”
Wan Muhamad Taufik
Associate Data Scientist
"The instructors went above and beyond to allay our fears. They assigned us an enormous amount of work, including one very difficult live project. great location for studying.”
Venu Panjarla
AVP Technology
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And more...
The data science profession has given rise to a multitude of sub-domains although most of the responsibilities overlap there are subtle and pertinent differences in each of the roles. See below for a short description of what each of the roles represents. Please be wary that depending on the organizational structure and the industry, the roles may have different meaning but this should serve as a basic guideline.
A Data Analyst is tasked with Data Cleansing, Exploratory Data Analysis, and Data Visualization, among other functions. These responsibilities pertain more to the use and analysis of historical data for understanding the current state. So simply put, a Data Analyst can answer the question ‘what happened?’
A Data Scientist on the other hand will go beyond a traditional analyst and build models and algorithms to solve business problems using statistical tools such as Python, R, Spark, Cloud technologies, Tableau etc. The data scientist has an understanding of ‘what happened’ but will typically go a bit further to answer ‘how we can prevent/predict that from happening?’
A Data Engineer is the messenger that carries or moves data around. He is responsible for the data ingestion process, building data pipelines to make it flow seamlessly across source, target systems and also responsible for building the CI/CD (continuous integration, continuous development) pipelines.
A Data Architect has a much broader role that involves establishing the hardware and software infrastructure needed for an organization to perform Data Analysis. They help in selecting the right database, servers, network architecture, GPUs, cores, memory, hard disk etc.
There is a huge disparity in how these terms are used, sometimes DS, DA and BA are used interchangeably. Although, the gap is narrowing now, BA is strictly dealing with advanced analytics but DS is more about bringing predictive power using machine learning techniques. One thing is clear, Data Modelling typically means designing the scheman etc. Though there are no hard rules that distinguish one from another, you should get the role descriptions clarified before you join an organization.
The US market is currently going through an unprecedented economy and the job growth has also been the best in recent times. Multiple reputed sources are documenting the acute shortage of data science professionals. Our program aims to address this by preparing the candidates not only by providing theoretical concepts, but helping them learn by doing. You will also greatly benefit from doing a Live project through Innodatatics, a leading Data Analytics company which will prepare you in dealing with implementing a data science project end-to-end.
It has been well documented that there is a startling shortage of data science professionals worldwide and for the US market in particular. Now the onus is on you, the candidate and if you can demonstrate strong knowledge of Data Science concepts and algorithms, then there is a high chance for you to be able to make a career in this profession.
To help you achieve that 360DigiTMG provides internship opportunities through Innodatatics, our USA-based consulting partner, for deserving participants to help them gain real-life experience. You will be involved in executing a project end to end and this will help you with gaining the job training to help you in this career path.
There are numerous jobs available for data science professionals. Once you finish the training, assignments and the live projects successfully, we will circulate your resume to the organizations with whom we have formal agreements on job placements. We also conduct regular webinars to help you with your resume and job interviews. We cover all aspects of post-training activities that are required to get a successful placement.
After every classroom session, you will receive assignments through the online Learning Management System. Our LMS is a state-of-the-art system which facilitates learning at your convenience. We do impose a strict condition – you will need need to complete the assignments in order to obtain your data scientist certificate.
Since this course is a blended program, you will be exposed to a total of 80 hours of instructor-led live training. On top of that you will also be given assignments which could have a total duration running into 60-80 hours. In addition to this, you will be working on a live project for a month. All of our assignments are carried out online and the datasets, code, recorded videos are all accessed via our LMS.
We understand that despite our best efforts, sometimes life happens. In such scenarios you can access all of the course videos in the LMS.
Each student is assigned a mentor during the course of this program. If the mentor determines additional support is needed to help the student, we may refer you to another trainer or mentor.
If you are interested in making discoveries and learning new things then this is the field for you as every role in data science presents itself with new challenges, You can work as a Data Scientist, Data Analyst/Business Analyst, Data Engineer, Big Data Manager or a Machine Learning Engineer.
Data Scientist is a professional who has a versatile skill set to unravel the world of Big Data. The salary of a data scientist in the USA depends on many variables like experience, skillset, and location. The average salary of a data scientist in the USA is $95,470 per year.
Projects are a great way to understand the key areas where one needs to improve and upscale their skills in and find a way forward. The projects you can take up include converting any image into a 3D photo, face recognition project, object detection project, or analysis of a soccer game.
Open source tools are rising in popularity as these tools aid in the development of complex processing and assist in analyzing large data sets and also provide accessible data manipulation and graphing along with the visualization of data.
The course in the USA is designed to suit the needs of students as well as working professionals. We at 360DigiTMG give our students the option of both classroom and online learning. We also support e-learning as part of our curriculum.
Data science is fuel for many industries across various sectors like banking, finance, manufacturing, healthcare, transport, e-commerce, fraud and risk detection, internet search, airline route planning, advanced image and speech recognition, etc.
360DigiTMG offers customised corporate training programmes that suit the industry-specific needs of each company. Engage with us to design continuous learning programmes and skill development roadmaps for your employees. Together, let’s create a future-ready workforce that will enhance the competitiveness of your business.
Student Voices
4.8
I've reached a major milestone in my Data Analytics internship with 360DigiTMG. With guidance from experienced mentors, they’ve really helped me get closer to reaching my goals. Embrace the valuable knowledge and skills gained and continue leveraging this opportunity to excel in the dynamic field of data analytics.
I'm Sai Manikanta, delighted to share my internship journey at 360DigiTMG. This internship has been a great opportunity for me to expand my limits and gain new skills. Diverse activities provided profound insights, shaping a promising future. Grateful for this opportunity, I eagerly anticipate forthcoming outcomes.
The data analytics program was truly outstanding! The meticulously structured classes and enthusiastic instructors made learning both enjoyable and engaging. With this extensive knowledge at my disposal, I am not only confident but also eager to make significant strides in the field of data analytics.
One of the best institutes for training in Hyderabad. I am done with the Data science and Machine Learning course here. Trainers are highly educational and instructive. Invaluable experience gained through live projects, enhancing technical familiarity. Additional value provided through helpful working sessions further enriches the learning journey.
The teacher and staff are highly skilled at their jobs. They teach in a way that's easy to understand and interesting. They know a lot about the subject, so learning from them is great. The teacher plans everything well and explains hard stuff with lots of examples using Excel.
It was a wonderful experience for me as an intern to work in 360digitmg. This internship had made me become an expert in the field of data analytics which had greatly motivated me and Working with real-time datasets provided invaluable experience, enhancing my skills significantly.
It was an awesome experience at 360Digitmg, offering the best resources and fostering excellent interaction. Working on real-life projects under expert supervision provided invaluable learning opportunities. Overall, it was a highly rewarding learning experience that contributed significantly to my growth and career advancement.
I found a great coaching institute in Chennai for data-related courses. I completed a successful data analytics program there. The trainers were skilled and supportive, especially Vijay, who made learning Python easy. Thanks to him and 360DigiTMG. I also learned Data Analytics with SQL, Tableau, and Excel.
360DigiTMG institute offers an exceptional learning experience, excelling in data science and machine learning. Despite lacking coding background, tutors ensured effective learning, making concepts easily understandable. Tutorial sessions covered job interview prep and case studies, with Mind maps boosting confidence. Highly recommend this Bangalore institute for data-related courses.
Excited for upcoming internships, confident in my improved skills from the program. Explored new territories and gained invaluable experience. Ready to apply newfound knowledge and continue growing in future opportunities. Grateful for the journey so far, eager for what's ahead.
360DigiTMG institute offers one place where the course curriculum is so good and teacher training, equipping students with skills for their dream job. Grateful for the internship experience, including live projects, resume building, presentation practice, and interview preparation sessions. Enhanced confidence for future interviews. Thank you, 360DigiTMG, for the invaluable learning journey.
The data analytics with python course in the best coaching centre in Chennai. Finished the course well and worked on practical tasks. This helped me build my professional experience. By participating in interview preparation and project presentation sessions, I realized that I could present myself confidently to an interview.
During my internship at 360DigiTMG, I gained invaluable experience, expanding my knowledge significantly. The opportunity provided a rich learning environment, fostering personal and professional growth. Grateful for the wonderful experience and the skills acquired, which will undoubtedly shape my future endeavours.
Great institute! Exceptional learning experience, especially in data science and machine learning. Tutors adeptly simplified complex concepts despite my coding limitations. Varied tutorial sessions prepared us for job interviews with insightful case studies. Mind maps boosted confidence. Highly recommend this Bangalore-based institute for data-related courses.
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