Data Science Self Paced

Krutanic Data Science for program course assists in launching your data scientist journey, develop data science competencies, grasp Python & Machine Learning, scrutinize & illustrate substantial data, and acquire the expertise to fabricate machine learning models. It instructs you in the fundamentals of information science and aids in cultivating the practical abilities necessary to recognize structures, tendencies, and garner discernments from unprocessed data.

Information science stands out as one of the most sought-after occupations of this era, and the need for adept data scientists has never been more pronounced. Enterprises of all magnitudes are actively seeking data science experts who can scrutinize data, unearth valuable perspectives, and articulate the outcomes to buttress and authenticate significant corporate resolutions. Krutanic data science training initiative assists you in assimilating the role of a data scientist.

CURRICULAM

Module 1:
Introduction to Data Science Introduction to the field of data science, its importance, and its applications in various industries. Overview of basic data concepts and tools used in data analysis.

Module 2:
Data Manipulation and Preprocessing Techniques for cleaning and preparing data for analysis. Covers data wrangling, transformation, and handling missing values to ensure data quality.

Module 3:
Exploratory Data Analysis Exploring data through visualizations and statistical methods. Discovering patterns, trends, and outliers in datasets to inform further analysis.

Module 4:
Statistical Analysis and Hypothesis Testing Fundamentals of statistics for data-driven decision-making. Covers hypothesis testing, confidence intervals, and inferential statistics for drawing meaningful conclusions from data.

Module 5:
Machine Learning Fundamentals Introduction to machine learning concepts, algorithms, and model evaluation. Understanding supervised and unsupervised learning and their applications.

Module 6:
Supervised Learning Algorithms In-depth study of supervised learning techniques, including regression and classification. Implementing and fine-tuning machine learning models for prediction tasks.

Module 7:
Unsupervised Learning and Clustering Exploration of unsupervised learning methods, particularly clustering algorithms. Grouping similar data points and discovering hidden patterns in datasets.

Module 8:
Deep Learning and Neural Networks Introduction to deep learning and neural networks. Building and training artificial neural networks for complex tasks like image recognition and natural language processing.