Data Engineer

Data Visualization: Creating visual representations of data to extract insights and communicate findings effectively.

Predictive Analysis: Analyzing historical data to make predictions about future outcomes.

Statistical Modeling: Building mathematical models to analyze relationships within data.

Data Preprocessing: Cleaning, transforming, and preparing data for analysis.

Clustering and Classification: Grouping data points into clusters or assigning them to predefined classes.

Time Series Analysis and Forecasting: Analyzing time-dependent data and making predictions about future values.

Machine Learning: Designing and implementing algorithms that enable computers to learn from data.

Deep Learning Algorithms: Utilizing neural networks with multiple layers to solve complex problems.

Python Packages

Pandas: For data manipulation and analysis.

Numpy: For numerical computing with arrays and matrices.

Matplotlib: For creating static, interactive, and animated visualizations in Python.

Scikit-Learn: For machine learning algorithms and tools.

Scipy: For scientific computing and advanced mathematics.

Spacy: For natural language processing (NLP) tasks.

TensorFlow: For building and training deep learning models.

PySpark: For working with big data and distributed computing using Apache Spark with Python.