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PreviewSession 01 - Course Introduction (48:18)
StartSession 02 - Why Python requires for Machine Learning (16:18)
StartSession 03 - Intelligence - Data, Life Cycle of Data, AWS Storage Service, AWS S3 (34:57)
StartSession 04 - AWS Storage (S3, Upload, Buckets, Regions), Data Center (39:18)
StartSession 05 - Data (Synchronization, Migration), S3, CLI (Command Line Interface) (20:34)
StartSession 06 - Storage - S3, Accounts (40:09)
StartSession 07 - ML Server (31:07)
StartSession 08 - EC2 - Create Server, Connectivity (38:25)
StartSession 09 - AWS CLI (40:27)
StartSession 10 - EC2, Instances (Instances Types) (30:41)
StartSession 11 - EC2 Instance Family (45:56)
StartSession 12 - Opex, Capex, ROI, SLA (55:31)
StartSession 13 - ML Server (39:35)
StartSession 14 - Compute (User Data, Meta Data) (42:28)
StartSession 15 - Continuation (38:59)
StartSession 16 - Prepare Computation for ML Server (55:02)
StartSession 17 - ML Server (Deploy Package Manager) (51:30)
StartClasses are in progress....
Who to Join Data Science course
- IT Professionals
- Data Analysts
- Business Analysts
- Functional Managers
- Post Graduates
Advantages fo Self-Learning
- No time pressure
- No need for a schedule
- Improves memory
- Suitable for different learning styles
For Example:If you can buy a course for ₹999. We will be given to you all the related videos at the free of cost.
So you will get multiple videos sets access by one-time purchase.
For Example: If the same faculty held classes in future for the same course then you can access all of those videos.
1) Is data science good for freshers?
The companies do hire freshers for data analyst and data scientist positions. Over of the entry-level analytics jobs in India don't need any specialization or post-graduation. Students qualification you need in these companies is an Engineering Degree, and even the stream doesn't matter.
2) how to move into data science?
the best learn data mining and data science by doing, so start analyzing data as soon as you can! However, don't forget to learn the theory, since you need a Best statistical and machine learning foundation to understand what you are doing and to find real nuggets of value in the noise of Big Data.
only 7 steps to learn data mining many of these steps you can do in parallel:
- step 01 Learn R and Python
- step 02 Read 1-2 introductory books
- step 03 Take 1-2 introductory courses and watch some webinars
- step 04 Learn data mining software suites (https://svrtechnologies.com/tutorials/data-science-tutorial/)
- step 05 Check available data resources and find something there
- step 06 Participate in data mining competitions
- step 07 data scientists and via social network, groups, and meetings
3) how to get a job in data science?
Step 1: On my behalf Best suggestions then to get a job an as a Data Scientist:Step 2: studying important concepts, programming and answering business questions, also remember that you will be an important piece of the organization, but you will also deal with different types of situations, be ready to answer questions about how would you behave in different work problems.
Step 3: The people interviewing you too.
Step 4: They want you to get in the company, that’s a powerful advise that I remember every day.
You follow SVR post on “A day in the life of a Data Scientist” to have a better idea of what we do.
4) how to get certified in data science?
Steps to Becoming a Certified IT Professional
- Complete a Post secondary Education Program
- Gain Work Experience
- Become Certified
- Earn a Bachelor's Degree
5) why data science?
Data Science is successfully (python videos) Running value to all the business types by using statistics and deep learning to make good decisions and improve hiring.
Its also be used to crunch the previous data science and predict possible (data science tutorial) situations so that we can work on avoiding them.
6) how to learn data science?
Structure of students for beginners Steps:
1: Assessing your technical & Structured thinking skills.
2: A few more ML algorithms.
3: Pick up a data visualization tool.
4: Big Data tools and techniques (data science training).
5: Deep Learning Basic and Advanced.
6: Reinforcement Learning.
A learning planing for Data Science is necessary to become a successful data scientist For beginners and transitioners, R, Python, basic of statistics, basic and advanced machine learning algorithms form the plan. For intermediate or Graduation students, advanced machine learning algorithms (data science tutorial, big data, deep learning, and reinforcement learning are required to be understood Practicing with datasets and an online SVR Technologies training plan help showcase your skills.
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