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Why Devfanclub Data Science & Machine Learning Program?
Dive into the world of data with our Data Science courses, where you’ll learn how to analyze complex datasets, extract valuable insights, and make data-driven decisions. From foundational concepts to advanced machine learning algorithms, our curriculum covers everything you need to succeed in this in-demand field. Our hands-on approach ensures that you gain practical experience through projects and case studies, preparing you for real-world challenges.
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Mentorship is a transformative journey of guidance and support, where experienced individuals provide valuable insights, advice, and encouragement to help others reach their full potential.
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Top Instructors are the pillars of excellence in education, possessing not only deep subject matter expertise but also a passion for inspiring and empowering learners.
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Projects and case studies are invaluable components of learning, providing hands-on experience and real-world application of concepts.
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Career counseling is a personalized process designed to assist individuals in making informed decisions about their career paths.
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Tools and languages are the building blocks of technical proficiency across various fields. In the realm of technology and data science.
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Learners and alumni form the vibrant community that drives continuous growth and excellence in education.
Data Science & ML
Beginners
Course Duration: 3 Months
Career Outcome
Data Analyst, Data Scientist, ML Engineer
What You Will Learn
> Python‌ ‌Programming‌ ‌and‌ ‌Logic‌ ‌Building‌
> Data‌ ‌Structure‌ ‌&‌ ‌Algorithms‌
> Structure‌ ‌Query‌ ‌Language‌ ‌(SQL)‌
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Data Science & ML
Masters
Course Duration: 6.5 Months
Career Outcome
Data Analyst, Data Scientist, ML Engineer
What You Will Learn
> Python‌ ‌Programming‌ ‌and‌ ‌Logic‌ ‌Building‌
> Data‌ ‌Structure‌ ‌&‌ ‌Algorithms‌
> Pandas‌ ‌Numpy‌ ‌Matplotlib‌
> Statistics‌
> Machine‌ ‌Learning‌
> Natural‌ ‌Language‌ ‌Processing‌
> Computer‌ ‌Vision‌‌
> Data‌ ‌Visualization‌ ‌with‌ ‌Tableau‌
> Structure‌ ‌Query‌ ‌Language‌ ‌(SQL)‌
> Big‌ ‌Data‌ ‌and‌ ‌PySpark‌
> Development‌ ‌Operations‌ ‌with‌ ‌Azure‌
> Projects‌ ‌and‌ ‌Git‌
Devfanclub Data Science & ML Application Process
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Career counseling is a valuable service aimed at guiding individuals in making informed decisions about their career paths.
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Easy registration simplifies the process of signing up for services or events, ensuring a seamless and hassle-free experience for users.
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Embarking on a journey of upskilling opens doors to endless opportunities for personal and professional growth.
What types of projects are incorporated within the scope of this Data Science course?
Participate in Projects from Top Companies to Advance as a Data Scientist or ML Engineer
Acquire hands-on experience with authentic datasets and projects crafted in partnership with prominent industry leaders.
Internet Security​
Internet security encompasses the practices, tools, and measures put in place to protect data and systems from unauthorized access, cyber threats, and malicious activities on the internet.
Optimizing Networks
Network optimization involves improving the performance, efficiency, and reliability of computer networks to enhance overall productivity and user experience.
Designing Products​
Designing products is the process of conceptualizing and creating physical or digital goods that meet the needs and desires of users. It involves understanding user requirements.
Enhancing User Experience
Enhancing user experience (UX) involves improving the overall interaction between users and products or services to ensure a positive and seamless experience.
Crafting Resumes
Practice Interview
Generate Tangible Impact with Your Enhanced Skill Set!
Companies seek data scientists and ML engineers who possess not only certification and technical proficiency but also a profound grasp of business acumen.
Devfanclub DATA SCIENCE & ML ROAD MAP​
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Python‌ ‌Programming‌ ‌and‌ ‌Logic‌ ‌Building‌
I will prefer Python Programming Language. Python is the best for starting your programming journey. Here is the roadmap of python for logic building.
> Python basics, Variables, Operators, Conditional Statements
> List and Strings
> While Loop, Nested Loops, Loop Else
> For Loop, Break, and Continue statements
> Functions, Return Statement, Recursion
> Dictionary, Tuple, Set
> File Handling, Exception Handling
> Object-Oriented Programming
> Modules and Packages
2
Data Structure & Algorithms
Data Structure is the most important thing to learn not only for data scientists but for all the people working in computer science. With data structure, you get an internal understanding of the working of everything in software.
Understand these topics
> Types of Algorithm Analysis
> Asymptotic Notation, Big-O, Omega, Theta
> Stacks
> Queues
> Linked List
> Trees
> Graphs
> Sorting
> Searching
> Hashing
3
Pandas Numpy Matplotlib
Python supports n-dimensional arrays with Numpy. For data in 2-dimensions, Pandas is the best library for analysis. You can use other tools but tools have drag-and-drop features and have limitations. Pandas can be customized as per the need as we can code depending upon the real-life problem.
Numpy
> Vectors, Matrix
> Operations on Matrix
> Mean, Variance, and Standard Deviation
> Reshaping Arrays
> Transpose and Determinant of Matrix
> Diagonal Operations, Trace
> Add, Subtract, Multiply, Dot, and Cross Product.
Pandas
> Series and DataFrames
> Slicing, Rows, and Columns
> Operations on DataFrame
> Different ways to create DataFrame
> Read, Write Operations with CSV files
> Handling Missing values, replace values, and Regular Expression
> GroupBy and Concatenation
Matplotlib
> Graph Basics
> Format Strings in Plots
> Label Parameters, Legend
> Bar Chart, Pie Chart, Histogram, Scatter Plot
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Statistics
Descriptive Statistics
> Measure of Frequency and Central Tendency
> Measure of Dispersion
> Probability Distribution
> Gaussian Normal Distribution
> Skewness and Kurtosis
> Regression Analysis
> Continuous and Discrete Functions
> Goodness of Fit
> Normality Test
> ANOVA
> Homoscedasticity
> Linear and Non-Linear Relationship with Regression
Inferential Statistics
> t-Test
> z-Test
> Hypothesis Testing
> Type I and Type II errors
> t-Test and its types
> One way ANOVA
> Two way ANOVA
> Chi-Square Test
> Implementation of continuous and categorical data
5
Machine Learning
The best way to master machine learning algorithms is to work with the Scikit-Learn framework. Scikit-Learn contains predefined algorithms and you can work with them just by generating the object of the class. These are the algorithms you must know including the types of Supervised and Unsupervised Machine Learning:
> Linear Regression
> Logistic Regression
> Decision Tree
> Gradient Descent
> Random Forest
> Ridge and Lasso Regression
> Naive Bayes
> Support Vector Machine
> KMeans Clustering
Other Concepts and Topics for ML
> Measuring Accuracy
> Bias-Variance Trade-off
> Applying Regularization
> Elastic Net Regression
> Predictive Analytics
> Exploratory Data Analysis
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Natural Language Processing
To work on image and video analytics we can master computer vision. To work on computer vision we have to understand images.
> PyTorch Tensors
> Understanding Pretrained models like AlexNet, ImageNet, ResNet.
> Neural Networks
> Building a perceptron
> Building a single layer neural network
> Building a deep neural network
> Recurrent neural network for sequential data analysis
Convolutional Neural Networks
> Understanding the ConvNet topology
> Convolution layers
> Pooling layers
> Image Content Analysis
> Operating on images using OpenCV-Python
> Detecting edges
> Histogram equalization
> Detecting corners
> Detecting SIFT feature points
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Computer Vision
To work on image and video analytics we can master computer vision. To work on computer vision we have to understand images.
> PyTorch Tensors
> Understanding Pretrained models like AlexNet, ImageNet, ResNet.
> Neural Networks
> Building a perceptron
> Building a single layer neural network
> Building a deep neural network
> Recurrent neural network for sequential data analysis
Convolutional Neural Networks
> Understanding the ConvNet topology
> Convolution layers
> Pooling layers
> Image Content Analysis
> Operating on images using OpenCV-Python
> Detecting edges
> Histogram equalization
> Detecting corners
>Detecting SIFT feature points
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Data Visualization with Tableau
How to use it Visual Perception
> What is it, How it works, Why Tableau
> Connecting to Data
> Building charts
> Calculations
> Dashboards
> Sharing our work
> Advanced Charts, Calculated Fields, Calculated Aggregations
> Conditional Calculation, Parameterized Calculation
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Structure Query Language (SQL)
> Setup SQL server
> Basics of SQL
> Writing queries
> Data Types
> Select
> Creating and deleting tables
> Filtering data
> Order
> Aggregations
> Truncate
> Primary Key
> Foreign Key
> Union
> MySQL
> Complex Questions
> Solving Interview > Questions
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BigData and PySpark
BigData
> What is BigData?
> How is BigData applied within Business?
PySpark
> Resilient Distributed Datasets
> Schema
> Lambda Expressions
> Transformations
> Actions
Data Modeling
> Duplicate Data
> Descriptive Analysis on > Data
> Visualizations
> ML lib
> ML Packages
> Pipelines
Streaming
> Packaging Spark Applications
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Development Operations with Azure
> Foundation of Data Systems
> Data Models
> Storage
> Encoding
> Distributed Data
> Replication
> Partitioning
> Derived Data
> Batch Processing
> Stream Processing
> Microsoft Azure
> Azure Data Workloads
> Azure Data Factory
> Azure HDInsights
> Azure Databricks
> Azure Synapse Analytics
> Relational Database in Azure
> Non-relational Database in Azure
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Generative AI
> Introduction to AI
> History of AI
> Types of AI (Narrow vs. General)
> AI Applications in Various Fields
> Quiz/Review
> Introduction to Machine Learning
> Supervised Learning
> Unsupervised Learning
> Reinforcement Learning
> Quiz/Review
> Introduction to Neural Networks
> Convolutional Neural Networks (CNNs)
> Recurrent Neural Networks (RNNs)
> Generative Adversarial Networks (GANs)
> Quiz/Review
> Natural Language Processing (NLP)
> Computer Vision
> AI Ethics and Bias
> AI in Healthcare
> Final Project/Presentation
> Introduction to Generative AI
> GANs and Variational Autoencoders
> Deep Reinforcement Learning
> Transformers and Attention Mechanism
> Quiz/Review
> Introduction to Large Language Models
> GPT Architecture and Training
> Fine-tuning and Transfer Learning
Devfanclub Data Science & ML Training FAQ’s
Who should take this Data Science course?
This Data Science course is designed for everyone, even if you have no coding experience. We offer a Beginner module that covers the basics of coding to get you started.
Why is Devfanclub Data Science program considered one of the best data science courses?
Devfanclub Data Science and Machine Learning program is considered one of the best data science courses because-
– Covers all essential data science topics, ensuring a holistic learning experience.
– Emphasis on hands-on projects equips students with real-world skills, setting them up for success in the field.
– Industry experts as instructors provide invaluable insights and knowledge.
– Devfanclub industry connections and placement assistance enhance job prospects.
– The program caters to diverse backgrounds, offering flexibility in learning for all.
Can I attend this Data Science online course part-time?
Yes, you have the flexibility to attend Devfanclub Data Science online course on a part-time basis. In case you miss a live class, you can always access the recorded sessions. You can also take a break of up to 3 months, all this within the course duration.
What is the total duration of this Data science course?
While designing the Devfanclub Data Science course, we did not put any limit on the duration. We included each and every concept that is important for making you a strong Data Scientist and ML Engineer. The course turned out to be 15 months long with more hands-on experience.
When are the live classes held?
Live classes are held 3 times a week, on alternate days, primarily in the late evening or night on weekdays to accommodate working software engineers. Weekend timings are flexible.
Working on a project would take a lot of time. How will we cover 50+ such projects in 15 months?
25 data science projects will be assigned in homework and others will be covered in class.
In each case, we will focus on a particular portion that is relevant to the topic being covered that week.
Each case study is discussed in class, at the end of the week (Business Case Discussion Session)
Submissions from all students are posted on an internal Discussion Forum where you can read how other students have solved that particular problem.
Students are allowed to drop 25% of the total case studies, depending on their interest area. So they can focus the remaining time on other cases of their interest.
As students practice working under deadlines, their speed and productivity will hopefully start improving.
How will we cover all this in just 9 months?
While designing the Devfanclub Data Science course, we did not put any limit on the duration. We included each and every concept that is important for making you a strong Data Scientist and ML Engineer. The course turned out to be 15 months long with more hands-on experience.
Notice that the course is quite rigorous; each week you will have 3 Live lectures of 2.5 hours each, homework assignments, business case project, and discussion sessions. This allows us to cover the entire depth and breadth of Data Science & Machine Learning, as much as is required for you to succeed in the role.
What is the total tuition fee for this Data science course?
The total Data Science course fees is ₹70,000. With EMI, (equivalent to your monthly grocery bill!)
Is Devfanclub good for Data Science?
Absolutely! Devfanclub offers a top-notch data science course designed to equip you with the skills and knowledge needed to excel in this field. Our program emphasizes hands-on learning with real-world projects and 1:1 mentorship from industry experts. We believe in providing practical experience that translates directly to the workplace. With our comprehensive curriculum and career support services, Devfanclub is an excellent choice for anyone looking to kickstart or advance their career in data science.
Why Choose Devfanclub for Data Science?
– Get to work on 50+ hands-on projects and real-world case studies to enrich your learning experience.
– Get 1:1 Mentorship from Expert Data Scientists and ML Engineers!
– Up-to-date curriculum with the fast-evolving Data Science and ML field.
– Master essential tools and languages used in data science and machine learning.
– Get Expert career guidance to help you navigate your path in data science.
Is there any eligibility test for enrolling in Devfanclub Data Science program?
Yes, there is an eligibility test called the Devfanclub entrance test for enrolling in Devfanclub Data Science program.
What skills will you learn in this Data Science certification course?
In Devfanclub Data Science certification course, you’ll acquire a wide range of skills, including:
– Beginner skills in Tableau, Excel, SQL, and Python.
– Data analysis and visualization using Python libraries, probability, and statistics.
– Foundations of machine learning, deep learning, and neural networks.
– Specializations in either machine learning or deep learning.
– Advanced knowledge in machine learning operations, data structures, and algorithms to excel in the field.
Who is eligible for Devfanclub Data Science course?
Devfanclub Data Science and Machine learning program is open to both freshers and working professionals. who are comfortable and confident with 10 standard aptitudes and mathematics.
I don’t have coding background. Can I enroll in this Data Science program?
A coding background is not required to enroll in this Data Science training. You can start from the Beginner module in which we will cover the basics of coding.
In fact, prior knowledge in Data Science or ML is also not needed. We will cover all the relevant topics from scratch.
The only prerequisite is that you should have a basic understanding of 9th and 10th-grade school maths – just the basics, nothing advanced. Still, we will cover these topics in class, but some prior knowledge would be helpful.
What is data science?
Data science is a field of computer science that uses various algorithms, methods, and machine learning to uncover hidden and meaningful insights in both structured and unstructured data.
Is data science easy to learn?
Data science can be challenging, as it requires a solid understanding of mathematics, statistics, and programming. However, with dedication and the right resources, it’s accessible to those willing to learn.
Who is a Data Scientist?
A data scientist is an expert in data science who specializes in collecting and analyzing large amounts of data from diverse sources. They use their skills in mathematics, statistics, and computer science to help organizations make informed decisions based on data analysis.
How do I become a Data Scientist?
To become a Data Scientist, follow these steps:
– Learn the fundamentals of programming and statistics.
– Acquire knowledge in machine learning and data analysis.
– Build a strong portfolio of projects.
– Pursue relevant courses.
– Apply for Data Scientist positions.
How is a Data Scientist different from a Data Analyst?
A Data Scientist designs new data approaches, while a Data Analyst interprets existing data. Data Scientists create innovative ways to collect and analyze data, while Data Analysts extract insights from available data.
Is Data Science a good career in 2024?
Yes, Data Science is an excellent career choice in 2024. The field is growing rapidly, with high demand for professionals due to its continued relevance and the increasing importance of data-driven decisions.
What are the job opportunities after completing this data science course?
After completing the data science course, you can explore various job roles, including:
– Business Analyst
– Data Analyst
– Data Scientist
– Big Data Engineer
– Data Engineer
– Machine Learning Engineer
– Data Architect, and many more.
Which top companies are hiring data scientists?
Top companies like Amazon, Google, IBM, Oracle, Deloitte, Facebook, Microsoft, Wipro, Accenture, Visa, Bank of America, and Fractal Analytics are actively hiring data scientists.
Does the data science course offer job placement assistance or career support?
Absolutely! Scaler goes the extra mile to ensure your success. We provide resume building, referrals, mock interviews, and career counseling to help you land your dream job with top companies.
What is the average salary of a data scientist?
The average salary for a data scientist in India is ₹9.4 Lakhs per year, with a salary range of ₹3.7 Lakhs to ₹25.1 Lakhs.
How do I earn Devfanclub Data Science certification?
To earn Devfanclub Data Science certification, you need to successfully complete all the required course modules, assignments, and projects. You’ll be assessed based on your performance throughout the program.
What is the validity of Devfanclub Data Science certification?
Devfanclub Data Science certification is a lifetime certification, meaning it doesn’t expire. Once you earn it, you can proudly showcase your expertise in data science throughout your career.
Can I get a Data Science course completion certificate even if I don’t achieve a certain grade or score?
No, you won’t receive a Data Science course completion certificate without a certain grade or score, but you can obtain an enrollment document.
What is the credibility of Devfanclub Data Science certification?
Devfanclub Data Science certification is highly regarded in the industry. It’s recognized for its comprehensive curriculum and hands-on approach, making you job-ready.
What happens if I miss a lecture in this online data science course?
If you miss a lecture, you can still watch it offline, and it won’t affect your attendance.
Can I access course materials and lectures even after the course is completed?
Yes, you can access course materials and lectures for up to 6.5 months after completing the course.
What should I do if I find it challenging to balance my job or schedule with the class timings?
If you find it challenging to balance your job or schedule with class timings, you can catch up by watching the recorded lectures as classes are held three times a week on alternate days.
Is the data science course self-paced or instructor-led?
Devfanclub data science program is instructor-led, ensuring you have guidance and support throughout your learning journey.
How much will be the focus on maths as maths is the base for data science and machine learning?
All the Maths required for understanding and implementing algorithms will be covered in this Data Science training (Probability, Statistics, Linear Algebra, Calculus, Coordinate Geometry).
What support channels are available for students to ask questions or seek help?
Devfanclub offers multiple support channels for students, including community Slack and WhatsApp groups for collaboration, dedicated problem-solving support on the dashboard, and Devfanclub support through email, chat, and phone for any concerns or queries.
Is there Devfanclub community or forum where students can interact and collaborate with each other?
Yes, there is a Devfanclub community where students can interact and collaborate with each other.
Does the Devfanclub community have people working outside India? I see a lot of good companies have data science teams outside India and I also wanted to work outside India.
The Devfanclub community has people working worldwide. The bottleneck is in getting a visa sponsorship. Many companies based in India offer opportunities for their high-performing employees to work on international data science projects and relocate. Some international companies also hire directly in India and ask to relocate for jobs. However, with the surge in WFH, this trend may be ebbing. However, you can continue applying for remote data science jobs based outside India via LinkedIn.
Will there be help and facilities provided for writing research papers?
For learners who show interest in publishing in the data science domain, we would be happy to provide mentorship and support.
How will Devfanclub Data Science program be more effective than a Masters? I saw a few companies’ JD today which mentioned that MS or PhD is required.
Masters and Ph.D.s are typically asked for Research-focused data science roles. Most companies do not require a Master’s degree for a Data Science role.
Need Help? Talk to us at +91 9209997139 or Request Callback
Contact
- contact@devfanclub.com
- +91 9209997139