Description
Course Name: Certificate in Data Analysis Deep Learning
Course Id: CDADL/Q1001.
Education Qualification: 12th Pass.
Duration: 90 Hrs.
How You will Get Diploma Certificate:
Step 1- Select your Course for Certification.
Step 2- Click on Enroll Now.
Step 3- Proceed to Enroll Now.
Step 4- Fill Your Billing Details and Proceed to Pay.
Step 5- You Will be Redirected to Payment Gateway, Pay Course and Exam Fee by Following Options.
Card(Debit/Credit), Wallet, Paytm, Net banking, UPI and Google pay.
Step 6- After Payment You will get Study Material Login id and Password on your email id.
Step 7- After Completion of Course Study give Online Examination.
Step 8- After Online Examination you will get Diploma Certificate soft copy(Scan Copy) and Hard Copy(Original With Seal and Sign).
Step 9- After Certification you will receive Prospect Job Opportunities as per your Interest Area.
Online Examination Detail:
Duration- 60 minutes.
No. of Questions- 30. (Multiple Choice Questions).
Maximum Marks- 100, Passing Marks- 40%.
There is no negative marking in this module.
| Marking System: | ||||||
| S.No. | No. of Questions | Marks Each Question | Total Marks | |||
| 1 | 10 | 5 | 50 | |||
| 2 | 5 | 4 | 20 | |||
| 3 | 5 | 3 | 15 | |||
| 4 | 5 | 2 | 10 | |||
| 5 | 5 | 1 | 5 | |||
| 30 | 100 | |||||
| How Students will be Graded: | ||||||
| S.No. | Marks | Grade | ||||
| 1 | 91-100 | O (Outstanding) | ||||
| 2 | 81-90 | A+ (Excellent) | ||||
| 3 | 71-80 | A (Very Good) | ||||
| 4 | 61-70 | B (Good) | ||||
| 5 | 51-60 | C (Average) | ||||
| 6 | 40-50 | P (Pass) | ||||
| 7 | 0-40 | F (Fail) | ||||
Benefits of Certification:
- Government Authorized Assessment Agency Certification.
- Certificate Valid for Lifetime.
- Lifetime Verification of Certificate.
- Free Job Assistance as per your Interest Area.
Syllabus
Data Visualization for Decision Making : Early maps and diagrams, Measurement and theory, New graphic forms, Beginnings of modern graphics, Golden age of statistical graphics, Modern dark ages, Rebirth of data visualization, High-d, interactive and dynamic data visualization.
Data Analytics Overview : Introduction to big data and analytics, Introduction to technology landscape, Mongodb and mapreduce programming, Data analytics with Machine learning, Classification of Digital Data, Supervised learning.
Social Network Analytics : Social network analysis, Software for social network analysis, Applications of network analysis, Block model role structure, Overlapping cliques in a social hierarchy, Strong and weak ties.
Prescriptive-and-Predictive-Analytics : Analytics and transforming the finance function, Case studies in predictive and prescriptive analytics, Sbv services (pty) limited, Large consumer packaged goods company, Cox industries, Encore enterprises inc.
Graphical Techniques : Evaluating structure scores, Evaluation of structure scores for a naive bayes clustering model, Persistence networks for diagnosis, Decision making for prenatal testing, Identifying the eect of smoking on cancer, Learning cellular networks from intervention data.
Design and Analysis of Algorithm : Divide-and-conquer, Prune-and-search, Dynamic programming, Greedy algorithms, Geometric graphs, Red-black trees, Heaps and heapsort, Alpha shapes, Easy and hard problems.
Job Opportunities after completion of Certificate in Data Analysis Deep Learning course:
Career Opportunities After Completion of the Certificate in Data Analysis & Deep Learning Program in India
The Certificate in Data Analysis & Deep Learning program provides graduates with the advanced skills required to analyze large datasets and apply deep learning techniques to solve complex problems. This interdisciplinary field combines knowledge of data analysis, machine learning, and deep learning to extract valuable insights from data, making graduates highly sought after in various sectors such as technology, finance, healthcare, and more. Below are the career options and salary ranges for graduates in India.
1. Career Opportunities
a) Data Scientist
- Role: Data Scientists use data analysis, machine learning, and deep learning techniques to analyze large datasets and extract actionable insights. They are responsible for designing data models, creating algorithms, and interpreting complex data to help businesses make informed decisions.
- Workplaces: Technology companies, financial institutions, healthcare providers, e-commerce businesses, consulting firms.
- Salary: ₹6–12 LPA (entry-level); ₹12–25 LPA (mid-level); ₹25–45 LPA (senior-level).
b) Machine Learning Engineer
- Role: Machine Learning Engineers design and develop algorithms that enable machines to learn from data. They create predictive models and integrate machine learning systems into applications to automate tasks and optimize business processes.
- Workplaces: Tech firms, research institutions, artificial intelligence (AI) startups, e-commerce companies.
- Salary: ₹7–14 LPA (entry-level); ₹14–25 LPA (mid-level); ₹25–40 LPA (senior-level).
c) Deep Learning Engineer
- Role: Deep Learning Engineers specialize in creating neural networks and deep learning models to solve complex problems like image and speech recognition, natural language processing (NLP), and autonomous systems. They work with advanced tools and frameworks like TensorFlow, Keras, and PyTorch.
- Workplaces: AI startups, technology companies, research and development labs, robotics companies.
- Salary: ₹8–15 LPA (entry-level); ₹15–30 LPA (mid-level); ₹30–50 LPA (senior-level).
d) Data Analyst
- Role: Data Analysts collect, clean, and analyze data to provide actionable insights for businesses. They use statistical tools and techniques to interpret data trends and patterns and help organizations make data-driven decisions.
- Workplaces: Corporations, consultancy firms, financial institutions, marketing companies, e-commerce platforms.
- Salary: ₹4–7 LPA (entry-level); ₹7–12 LPA (mid-level); ₹12–18 LPA (senior-level).
e) AI Researcher
- Role: AI Researchers conduct experiments and studies to advance the field of artificial intelligence. They work on developing new algorithms, improving existing models, and exploring innovative applications of AI and deep learning.
- Workplaces: Research institutions, universities, AI labs, technology companies.
- Salary: ₹7–12 LPA (entry-level); ₹12–25 LPA (mid-level); ₹25–40 LPA (senior-level).
f) Data Engineer
- Role: Data Engineers design, construct, and maintain the systems and infrastructure required for collecting, storing, and processing large amounts of data. They ensure that data is available for analysis and build data pipelines that facilitate the work of data scientists and analysts.
- Workplaces: Tech firms, e-commerce companies, finance firms, healthcare providers.
- Salary: ₹6–12 LPA (entry-level); ₹12–20 LPA (mid-level); ₹20–40 LPA (senior-level).
g) Business Intelligence Analyst
- Role: Business Intelligence Analysts use data analysis to create reports, dashboards, and visualizations that provide valuable insights into business performance. They analyze sales, marketing, and customer data to help organizations optimize their strategies.
- Workplaces: E-commerce companies, marketing firms, financial institutions, and consulting agencies.
- Salary: ₹5–9 LPA (entry-level); ₹9–15 LPA (mid-level); ₹15–30 LPA (senior-level).
h) Quantitative Analyst (Quant)
- Role: Quantitative Analysts use deep learning, statistical models, and data analysis techniques to assess and predict financial trends, stock prices, and investment risks. They work in investment banks, hedge funds, and financial trading firms.
- Workplaces: Investment banks, hedge funds, insurance companies, financial consultancies.
- Salary: ₹10–20 LPA (entry-level); ₹20–35 LPA (mid-level); ₹35–50 LPA (senior-level).
i) Natural Language Processing (NLP) Engineer
- Role: NLP Engineers specialize in enabling machines to understand and generate human language. They work on projects like speech recognition, chatbots, translation tools, and sentiment analysis using deep learning models.
- Workplaces: AI startups, research institutions, tech companies, e-commerce platforms.
- Salary: ₹7–12 LPA (entry-level); ₹12–25 LPA (mid-level); ₹25–40 LPA (senior-level).
j) Computer Vision Engineer
- Role: Computer Vision Engineers use deep learning techniques to create systems that allow machines to interpret and understand visual data. Applications include facial recognition, object detection, and autonomous vehicles.
- Workplaces: Technology companies, robotics firms, AI labs, research institutions.
- Salary: ₹8–15 LPA (entry-level); ₹15–30 LPA (mid-level); ₹30–50 LPA (senior-level).
2. Industries That Employ Data Analysis and Deep Learning Graduates
- Technology & AI: Companies like Google, Microsoft, Amazon, and startups in AI and machine learning sectors.
- E-commerce: Giants like Flipkart, Amazon, and Myntra rely on data analytics to optimize supply chains, recommend products, and understand consumer behavior.
- Finance & Banking: Banks, insurance firms, and trading companies leverage deep learning for fraud detection, risk assessment, and algorithmic trading.
- Healthcare: Hospitals and pharmaceutical companies use data analytics for predicting diseases, managing patient data, and developing treatment models.
- Manufacturing: Industries use predictive analytics for production planning, supply chain management, and predictive maintenance.
- Automotive & Robotics: Companies like Tesla, Siemens, and ABB implement AI and machine learning for autonomous vehicles and robotics.
- Consulting & Research: Firms such as Deloitte, McKinsey, and KPMG, as well as universities, employ deep learning experts to analyze market trends and conduct research.
3. Salary Range and Career Growth
- Entry-Level Positions: ₹4–10 LPA for roles like Data Analyst, Machine Learning Engineer, and Deep Learning Research Assistant.
- Mid-Level Roles: ₹10–20 LPA for roles like Data Scientist, Deep Learning Engineer, and Quantitative Analyst.
- Senior-Level Roles: ₹20–50 LPA for experienced professionals in roles like Senior Data Scientist, AI Researcher, and Senior Machine Learning Engineer.
4. Skills for Success in Data Analysis and Deep Learning
- Proficiency in Programming Languages: Python, R, Java, and C++ are commonly used for data analysis and deep learning.
- Statistical Analysis: A strong understanding of statistics is essential for interpreting data trends and creating predictive models.
- Machine Learning Frameworks: Familiarity with tools like TensorFlow, PyTorch, Keras, Scikit-learn, and others is crucial for building models.
- Data Visualization: Skills in tools like Tableau, Power BI, and Matplotlib to present data insights effectively.
- Mathematical and Algorithmic Knowledge: Understanding of linear algebra, calculus, and optimization algorithms.
- Knowledge of Big Data Technologies: Familiarity with Hadoop, Spark, and cloud platforms for handling large datasets.
5. Additional Certifications and Courses for Career Advancement
- Certified Data Scientist (CDS): Offered by various institutions, this certification adds value by showcasing your ability to work on data analysis and machine learning projects.
- Deep Learning Specialization (Coursera): A program that covers the core concepts of deep learning, taught by Andrew Ng.
- AWS Certified Machine Learning – Specialty: A certification by Amazon Web Services, valuable for those working with cloud-based machine learning models.
- Google Professional Machine Learning Engineer Certification: A certification focused on applying ML to real-world problems using Google Cloud services.
Conclusion
Graduates of the Certificate in Data Analysis & Deep Learning program are in high demand due to the growing reliance on data to drive business decisions and technological advancements. With a combination of strong analytical skills and deep learning expertise, professionals can work in a wide variety of industries, from technology to finance to healthcare. The salary range is highly competitive, and with the rapid growth in AI and machine learning, there is significant potential for career progression in this field. As organizations continue to focus on data-driven decision-making, the need for skilled data analysts and deep learning professionals will continue to rise.
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