Highly rated on Google
4.9/5
Course Duration
10 weeks
Learning Mode
Offline
Placement's*
100%
Description:
The Data Science Essentials course provides participants with a comprehensive understanding of key concepts, techniques, and tools used in data science. Participants will learn about data manipulation, data visualization, statistical analysis, machine learning, and big data technologies. Through a combination of theoretical learning and hands-on exercises using Python and popular data science libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn, participants will gain proficiency in extracting insights from data and building predictive models.
Key Topics:
- Introduction to Data Science and the Data Science Lifecycle
- Data Collection, Cleaning, and Preprocessing
- Exploratory Data Analysis (EDA) and Data Visualization
- Statistical Analysis and Hypothesis Testing
- Introduction to Machine Learning and Supervised Learning
- Linear Regression and Logistic Regression
- Decision Trees and Random Forests
- Clustering Algorithms (K-means, Hierarchical Clustering)
- Dimensionality Reduction Techniques (PCA, t-SNE)
- Model Evaluation and Validation
- Introduction to Deep Learning and Neural Networks
- Big Data Technologies (Hadoop, Spark) for Data Science
- Introduction to Natural Language Processing (NLP) and Text Mining
Prerequisites: Basic understanding of programming concepts and familiarity with Python programming language is recommended. Some knowledge of statistics and mathematics would be beneficial but not mandatory.
Who Should Attend:
- Data enthusiasts, analysts, and professionals interested in entering the field of data science.
- IT professionals seeking to transition into data science roles.
- Students and recent graduates looking to build a career in data science or related fields.
Outcome:
Upon completion of the course, participants will have a solid understanding of data science fundamentals and be able to perform data analysis, build predictive models, and derive insights from data. They will be equipped with the skills necessary to work effectively with data and contribute to data-driven decision-making within organizations.