- Python π: Mastering programming for data manipulation, analysis, and machine learning.
- SQL π’οΈ: Querying and managing databases efficiently.
- CRM Analytics π: RFM Analysis- CLTV - CLTV Prediction
- Measurement Problems π: Rating Products, Sorting Products, Sorting Reviews, A/B Testing, Statical Hypothesis
- Recommendation Systems π: Association Rule Learning, Item and User Based Collaborative Filtering, Content-Based Recommendation, Model-Based Matrix Factorization
- Feature Engineering π οΈ: Outliers, Missing Values, Encoding Scaling, Feature Extraction)
- Machine Learning π€: Designing and deploying predictive models.
- Deep Learning π§ : Building neural networks and advanced AI models.
- Big Data Tools ποΈ: Proficiency in Hadoop, Spark, or similar technologies.
- Statistics & Probability π²: Foundation of hypothesis testing and data insights.
- Data Wrangling π§Ή: Cleaning and preprocessing messy data.
- Data Visualization π: Communicating findings through compelling visuals (e.g., Tableau, Matplotlib, Seaborn).
- A/B Testing βοΈ: Designing experiments for data-driven decision-making.
- Soft and Interdisciplinary Skills:
- Critical Thinking π§©: Solving complex problems with logical reasoning.
- Communication π£οΈ: Translating technical findings into actionable insights for stakeholders.
- Business Acumen πΌ: Understanding the context and application of data science solutions.
- Collaboration π€: Teamwork with engineers, analysts, and business leaders.
- Natural Language Processing (NLP) π£οΈπ»: Extracting insights from text data.
- Computer Vision ποΈβπ¨οΈ: Analyzing image and video data.
- Cloud Computing βοΈ: Deploying models and data pipelines (e.g., AWS, Azure, GCP).
- Generative AI β¨: Creating content with models like GPT or DALLΒ·E.
- TensorFlow & PyTorch π§: Frameworks for deep learning.
- Pandas & NumPy πΌπ: Data manipulation and numerical analysis.
- Scikit-Learn π: Standard machine learning library.
- Git/GitHub π§βπ»: Version control and collaboration.
and
- π₯οΈ Programming Languages: Proficient in CCS C, C++, and embedded systems programming.
- π‘ Microcontroller Programming: Experienced in programming PIC and ESP32 microcontrollers.
- βοΈ Embedded System Design: Knowledge in designing and integrating embedded systems.
- π Hardware Interface: Handling GPIO, ADC, timers, and other peripheral integrations.
- π‘ Communication Protocols: Working with I2C, SPI, UART for sensor and device communication.
- π§ PWM and ADC/DAC: Control motors, analog signals using PWM and ADC.
- Wi-Fi and Bluetooth: Expertise in wireless communication (Wi-Fi, Bluetooth) with ESP32.
- PCB Design: Experience in schematic design and PCB prototyping.
- IDE and Tools: Proficient in IDEs like MPLAB X, CCS, PlatformIO, and Arduino IDE.
- Firmware Development: Developing, updating, and debugging embedded firmware.
- Debugging: Skilled in debugging and error resolution for embedded systems.
- IoT Development: Building IoT projects and integrating sensors with cloud services.
- Cloud Integration: Sending data from microcontrollers to cloud platforms (e.g., AWS, ThingSpeak).
- Data Analysis: Collecting and analyzing sensor data for decision-making.
- Simulation Tools: Using tools like Proteus and Tinkercad for simulation of embedded systems.
- Team Collaboration: Experience working in teams, managing project timelines, and deliveries.
