Data Science Engineer Roadmap
Key Competencies and Knowledge
for a Successful AI Engineering Career
for a Successful AI Engineering Career
Trusted by
You Know
SQL
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Neural Networks
- Deep Learning
- Evaluation Metrics
- Model Optimization
- Feature Engineering
- Bias-Variance Tradeoff
- Model Deployment
- 9h 15m
Github
- Introduction to Git
- Git Setup
- Basic Commands
- Branching and Merging
- Working with Remotes
- Conflict Resolution
- Git Log and History
- Stashing Changes
- Tagging
- Best Practices
- 9h 15m
Python
- Variables and basic data types
- Loops, and functions
- Error handling and exceptions
- File input/output operations
- Using Python modules and libraries
- Python classes and objects
- Python virtual environments
- Basic debugging techniques in Python
- 9h 15m
Statistics & Analytics Techniques
- Descriptive Statistics
- Probability Theories And Applications
- What is Big Data
- Inferential Statistics For Decision Making
- Understanding Mean, Median, And Mode
- Measures Of Spread
- Shape Of The Distribution
- Data Visualization
- 9h 15m
Power BI
- Desktop Navigation
- Data Connectivity
- Transforming Data
- DAX Fundamentals
- Report Building
- Visualization Tools
- Service Publishing
- Collaboration Sharing
- Mobile Applications
- DAX Advancement
- 9h 15m
Azure PySpark
- Introduction to PySpark on Azure
- PySpark DataFrames
- What is Big Data
- Spark’s Basic Architecture
- Spark Toolset
- Spark Components
- Working with datasets using PySpark
- RDDs and transformations in PySpark
- DataFrames in PySpark
- Manipulating DataFrames
- 9h 15m
Azure Databricks
- Databricks Fundamentals
- Data Import and Export
- Cluster Management
- Notebooks in Databricks
- Data Engineering Pipelines
- Databricks SQL Analytics
- Stream Processing
- Machine Learning and AI
- Integrating with Azure Storage
- Security and Compliance
- 9h 15m
Advanced Python
(Pandas & NumPy)
- Data Manipulation With Pandas
- HPC With Numpy
- Using Pandas For Time Series
- Complex Data Transformations
- Memory Management In Python
- Optimization For Code Performance
- Integrating Python With Databases
- Matplotlib And Seaborn
- Scripting For Automation
- Advanced Error Handling Techniques
- 9h 15m
Data Science
- Introduction To Statistical Analysis
- Using Python For Data Cleaning
- Machine Learning Algorithms
- Data Visualization Techniques
- Manipulating Datasets Using Pandas
- Using Numpy For Numerical Data
- Data Importing/Exporting With Python
- Exploratory Data Analysis Techniques
- Introduction To Time Series Analysis
- Clustering And Classification Methods
- 9h 15m
Azure PySpark
- Introduction to PySpark on Azure
- PySpark DataFrames
- What is Big Data
- Spark’s Basic Architecture
- Spark Toolset
- Spark Components
- Working with datasets using PySpark
- RDDs and transformations in PySpark
- DataFrames in PySpark
- Manipulating DataFrames
- 9h 15m
Azure Databricks
- Databricks Fundamentals
- Data Import and Export
- Cluster Management
- Notebooks in Databricks
- Data Engineering Pipelines
- Databricks SQL Analytics
- Stream Processing
- Machine Learning and AI
- Integrating with Azure Storage
- Security and Compliance
- 9h 15m
Azure PySpark
- Introduction to PySpark on Azure
- PySpark DataFrames
- What is Big Data
- Spark’s Basic Architecture
- Spark Toolset
- Spark Components
- Working with datasets using PySpark
- RDDs and transformations in PySpark
- DataFrames in PySpark
- Manipulating DataFrames
- 9h 15m
Azure Databricks
- Databricks Fundamentals
- Data Import and Export
- Cluster Management
- Notebooks in Databricks
- Data Engineering Pipelines
- Databricks SQL Analytics
- Stream Processing
- Machine Learning and AI
- Integrating with Azure Storage
- Security and Compliance
- 9h 15m
Artificial Intelligence
- Definitions And Scope Of Ai
- Basic Models Of Machine Learning
- Natural Language Processing
- Ethical Considerations In AI
- Ai In Real-World Applications
- Simple Neural Networks
- Problem-Solving With AI
- Introduction To AI Programming Tools
- Basic Computer Vision Techniques
- AI Deployment Challenges
- 9h 15m
Data Science Engineer Level Achieved!
Data Science Engineer
Average Salary
$1,62,000 year
Concept BASED Learning
Frequently Asked Questions
• The Data Science Engineer Learning Path is a comprehensive educational program designed to equip learners with the skills needed to excel in data science roles. The curriculum covers a wide range of topics from data manipulation and statistical analysis to machine learning and data visualization techniques, supplemented with hands-on projects to apply learned skills in practical settings.
• The Data Science Engineer Learning Path is designed for a diverse range of professionals eager to develop or enhance their data science skills. Suitable participants include IT Professionals, Software Engineers, Data Analysts, Data Management Professionals, Finance Professionals, Marketing Professionals, Sales Professionals, HR & Recruiters, Engineers of various types, Managers across different sectors, Operations and Admin staff, Logistics and Supply Chain Industry professionals, Students and Academics in related fields, and Freelancers looking to offer data science services. This program is ideal for anyone looking to integrate data science into their current roles or to pivot to new career opportunities in the rapidly evolving field of data science and analytics.
The AI Engineer Roadmap is designed for individuals who have basic programming knowledge. The program covers everything from the ground up, starting with fundamental data types and extending to advanced AI and ML concepts, all paired with practical, real-world applications. This comprehensive approach ensures that participants not only learn theoretical aspects but also gain hands-on experience applying these technologies effectively.
The Data Science Engineer Learning Path equips you with essential data science skills. You will learn SQL for data management, Python for data analysis and visualization, and Git for version control. The program also includes practical machine learning applications, teaching you to build and deploy predictive models. Additionally, you'll learn to create interactive dashboards and visual reports for effective data-driven decision-making.
Typically, the path can be completed in 3 to 5 months, depending on your pace of study and previous experience.
Yes, the learning path includes several practical projects that challenge you to solve real-world problems using the data science skills you've acquired, enhancing both your understanding and your portfolio.
Learners receive comprehensive support through mentorship sessions with industry experts, access to peer collaboration forums, and technical support for hands-on project work. Regular webinars and guest lectures supplement the learning experience.
The program is delivered online through a mix of live interactive sessions and self-paced study materials, including video tutorials, reading materials, and real-time discussions.
Upon successful completion, you will receive a certificate from BotCampus AI, validating your expertise in data science, which enhances your resume and professional credibility.
Enroll directly through the BotCampus AI website. Visit the Enrollment page, select the Data Science Engineer Learning Path, and follow the registration instructions.
