Data Science

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Descriptions

Data is the new oil for all industries. Data science is the study of data. It involves developing methods of acquiring and analyzing data effectively, extracting useful information, and helping businesses to solve their priority problems/take decisions. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data science is related to data mining and big data. Data mining is a subset of data science.

Key Points

Course Lessons

Introduction to Data Science

Data science is the process of collecting, analyzing, and interpreting large volumes of data to drive business insights and decision-making. This module introduces the fundamentals of data science, including its interdisciplinary nature, which combines statistics, programming, and domain expertise. You will explore how data science is transforming industries and understand the different roles within the field, such as Data Analyst, Data Engineer, and Machine Learning Engineer.

Data Collection and Preprocessing

Raw data is often messy and unstructured, requiring careful preprocessing before analysis. In this section, you will learn different data collection methods, including APIs, databases, and web scraping. Techniques such as data cleaning, handling missing values, feature engineering, and data transformation will be covered. You will also get hands-on experience using tools like Pandas and NumPy for data manipulation and preparation.

Exploratory Data Analysis (EDA) and Data Visualization

EDA is a crucial step in understanding data patterns, trends, and relationships. You will use Python libraries like Matplotlib, Seaborn, and Plotly to visualize data effectively. Key concepts like histograms, scatter plots, box plots, correlation matrices, and feature importance will be discussed. You will also learn Tableau and Power BI to create interactive dashboards for business decision-making.

Statistical Analysis and Machine Learning Foundations

Data science is deeply rooted in statistics and probability. This module covers descriptive and inferential statistics, hypothesis testing, and regression analysis. You will then transition into machine learning, understanding supervised and unsupervised learning, classification, regression, and clustering. Algorithms such as Linear Regression, Decision Trees, and K-Means Clustering will be introduced, along with model evaluation techniques like confusion matrix and ROC curves.

Deep Learning and Artificial Intelligence

Deep learning is a subset of AI that focuses on neural networks to recognize patterns in data. You will learn about artificial neural networks (ANNs), convolutional neural networks (CNNs) for image processing, and recurrent neural networks (RNNs) for time-series forecasting. Using frameworks like TensorFlow and PyTorch, you will build and train deep learning models that can classify images, predict sequences, and enhance automation.

Big Data Technologies and Cloud Computing

With data growing exponentially, handling large-scale datasets is crucial. This section introduces Big Data frameworks like Apache Hadoop and Spark, allowing distributed data processing. You will explore SQL vs. NoSQL databases and cloud-based storage solutions, such as AWS, Google Cloud, and Azure. Concepts like ETL (Extract, Transform, Load), data pipelines, and real-time analytics will also be covered.

End-to-End Data Science Project

In this final module, you will integrate all learned concepts into a real-world data science project. You will work on a dataset, applying data cleaning, EDA, feature engineering, model building, and deployment. The project will involve business-oriented problem-solving, helping you gain hands-on experience. Additionally, you will learn how to showcase your work through GitHub, portfolio websites, and LinkedIn.