Data science is one of the hottest professions of the decade. Data scientists who can handle , analyze data and contribute to data driven decisions and products are the need of the hour . Data science is an interdisciplinary field focused on extracting knowledge and making better decisions in various domains such as banking, finance, entertainment, healthcare, agriculture, sensors, instrumentation and robotics increasing the value of a data scientist. Hence, this course on Applied Data Science with Python is designed with the following objectives:
- To expose the learners to the skills required to tackle and solve complex real-world data science problems more sensibly and effectively.
- To develop research interest towards advances in data science techniques and algorithms.
- To provide well-rounded insights into the data science algorithms and hands-on activities using python so that the learners acquire the necessary skills to successfully pursue and complete standard certifications.
COURSE DURATION: 4 Weeks
By the end of this course, the learners will be able to
- Analyze the need for data preprocessing and visualization techniques.
- Demonstrate the performance of different supervised learning algorithms like decision Tree, Random Forest, Linear Regression, Logistic Regression etc.
- Apply unsupervised learning algorithms like K-Means, K-Medoids etc for grouping the given data.
- Formulate and use appropriate models of data analysis to solve hidden solutions to business-related challenges.
- Apply modern data science methods to one or more domains of application (e.g. business analytics, finance, biotechnology, and public health)
Module -1- Introduction to Data Science, Python Data Structures, Python Numpy Package
Data Science - Need, Applications, Difference between data analysis and data analytics. Python- Variables, data types, control structures, Operators, Simple operations, Array and its operations, Numpy operations, Matrix and its operations
Module-2-Data preparation and preprocessing using Pandas dataframe, Exploratory Data Analysis, Data Visualization
Dealing missing values, Normalization, statistical description about the data, Accessing the data, Summary of the data, Relationship between the data, Data Visualization using matplotlib
Module-3-Supervised Learning Algorithms
Decision Tree Algorithm, ID3 Classifier, Random Forest, Classifier Accuracy, Linear Prediction, Logistic Regression – Case study
Module-4-Unsupervised Learning Algorithms
Various distance Function, Dissimilarity between the mixed types of data, K-Means Algorithm, K- Medoids Algorithm -Case Study
• Dr. C. Deisy
• Dr. S.Sridevi
• Dr. K.V.Uma