Idsb: Introduction To Data Science For Beginners

Unlock the world of data science and statistics with this beginner-friendly course

What you'll learn

You will learn Introduction to Data Science, which will cover the Role and Importance of Data Science in today's world

Learn an Overview of its applications across various industries, and the foundational principles that drive the field

Topics will include data collection, preparation for accuracy and reliability, and the ethical considerations of data handling

You will also explore core responsibilities and skills such as analyzing trends, solving problems, and understanding the lifecycle of data projects

Learn Core Statistical & Probabilistic Concepts. Descriptive Statistics for summarizing data, understanding measures of central tendency and variability

Learn exploring probability to quantify uncertainty. You will delve into Probability Distributions, Hypothesis Testing

Learn about Data Mining and Machine Learning Techniques. The discovery of patterns, clustering, and association analysis for insightful data exploration

Gain expertise in building classification models, understanding regression analysis, and learning predictive modeling

You’ll explore Algorithms like Decision Trees, Random Forests, Support Vector Machines, and Neural Networks, applying theoretical concepts to practical problem

Learn about Data Visualization and Interpretation Skills. This involves understanding the importance of visual representation for storytelling

Learning tools like Power BI and Tableau, and creating dashboards and reports that effectively communicate data insights. By mastering these skills

This training will be useful if your job involves roles and responsibilities in Advanced Data Science Applications

Requirements

You should have an interest in Data Science

An interest in Introduction to Data Science, which will cover the Role and Importance of Data Science in today's world

Have an interest in understanding Communication and Presentation Skills

Practical Applications and Career Development in Data Science

Description

Description· Take the next step in your career! Whether you’re an aspiring professional, experienced executive, or budding Data Science enthusiast, this course is your gateway to sharpening your Data Science capabilities and making a significant impact in your career or organization.· With this course as your guide, you learn how to: Understand core Data Science concepts, including data analysis, statistics, and machine learning, to solve real-world problems effectively.· Enhance your ability to apply theoretical knowledge to practical challenges in data handling, visualization, and prediction.· Gain proficiency in key tools and techniques like data mining, regression, and clustering for insightful analysis.· Develop a solid foundation for career advancement, with practical case studies, frameworks, and interactive exercises to hone your expertise.The Frameworks of the Course• Engaging video lectures, case studies, assessments, downloadable resources, and interactive exercises. This course is created to introduce you to Data Science, covering key concepts such as data collection, preparation, and analysis. You will learn about the role and responsibilities of a Data Scientist, and the importance of Data Science in solving real-world problems. Key topics will include understanding data accuracy, reliability, and core statistical concepts to interpret data and make informed decisions. You will also explore data mining techniques to effectively communicate findings, and dive into the basics of machine learning for predictive modeling.• Communication Skills: You will learn the importance of clear and effective communication in Data Science. Topics will cover verbal communication for presenting data insights, active listening techniques for collaborative problem-solving, and how to communicate findings effectively to non-technical stakeholders. You will also understand how to structure written reports, emails, and documentation in a professional way. Additionally, the course will cover non-verbal communication such as body language and how to build rapport with colleagues in Data Science teams. You will also explore office technology and tools relevant to Data Science, including software used for data visualization and analysis, as well as basic troubleshooting for Data Science-related tools.The course includes multiple case studies, resources such as templates, worksheets, reading materials, quizzes, self-assessments, and hands-on assignments to nurture and enhance your understanding of Data Science concepts. You will also have access to real-world data analysis projects where you can apply theoretical knowledge to practical issues, learning how to work with real datasets and make data-driven decisions.In the first part of the course, you’ll learn the fundamentals of Data Science, including an introduction to its key concepts such as data collection, data preparation, and the importance of data accuracy and reliability. You’ll explore the role and responsibilities of a Data Scientist and understand the critical skills required to work effectively in this field. This section will also cover core statistical concepts that form the foundation of data analysis and help in making informed decisions based on data.In the middle part of the course, you’ll develop your understanding of data analysis tools and techniques. You’ll gain hands-on experience with data visualization to present insights clearly, and learn the basics of machine learning for building predictive models. This section will also explore how to communicate data insights effectively, covering skills such as verbal communication, active listening, and presenting data findings to non-technical audiences. The course will also dive into office technologies relevant to Data Science, including essential software tools and advanced features of data analysis platforms.In the final part of the course, you’ll develop your skills in organizing and managing data workflows. You will learn how to prioritize data-related tasks, set data analysis goals, and use tools to track progress and manage projects effectively. You’ll also gain insights into how to plan and organize data-driven projects, including understanding how to structure data pipelines and coordinate team efforts. Additionally, you’ll receive continuous support with guaranteed responses to all your queries within 48 hours, ensuring that you can apply what you’ve learned to real-world data problems effectively.Course Content:Part 1Introduction and Study PlanIntroduction, Study Plan and Structure of the CourseModule 1: About to Data ScienceLesson 1: Overview of Data ScienceLesson 2: Major Application of Data ScienceLesson 3: Brief about Interdisciplinary FieldModule 2: StatisticsLesson 1: SamplingLesson 2: Descriptive StatisticsLesson 3: Hypothesis TestingLesson 4: RegressionLesson 5: ForecastingLesson 6: ANOVAModule 3: Probability and DistributionLesson 1: ProbabilityLesson 2: Mathematical Rules in ProbabilityLesson 3: Probability DistributionPart 2Module 4: Data MiningLesson 1: About Data MiningLesson 2: Data StructureLesson 3: Major ApplicationModule 5: Machine LearningLesson 1: Machine Learning TechniquesLesson 2: Other MethodsModule 6: Tools and FunctionLesson 1: Business Intelligent ToolsAssignment: Data Science

Overview

Section 1: Module 1: Introduction

Lecture 1 Introduction and Study Plan

Lecture 2 Overview of Data Science

Lecture 3 Major Application of Data Science

Lecture 4 Brief about Interdisciplinary Field

Section 2: Module 2: Statistics

Lecture 5 Lesson 1: Sampling

Lecture 6 Lesson 2: Descriptive Statistics

Lecture 7 Lesson 2: Descriptive Statistics 2

Lecture 8 Lesson 2: Descriptive Statistics 3

Lecture 9 Lesson 3: Hypothesis Test

Lecture 10 Lesson 3: Hypothesis Test 2

Lecture 11 Lesson 3: Hypothesis Test 3

Lecture 12 Lesson 4: Regression

Lecture 13 Lesson 4: Regression 2

Lecture 14 Lesson 4: Regression 3

Lecture 15 Lesson 5: Forecasting

Lecture 16 Lesson 4: Forecasting 2

Lecture 17 Lesson 5: Forecasting 3

Lecture 18 Lesson 6: ANOVA

Lecture 19 Lesson 6: ANOVA 2

Section 3: Module 3: Probability and Distribution

Lecture 20 Lesson 1: Probability

Lecture 21 Lesson 1: Probability 2

Lecture 22 Lesson 2: Mathematical Rules in Probability

Lecture 23 Lesson 2: Mathematical Rules in Probability 2

Lecture 24 Lesson 3: Probability Distribution

Lecture 25 Lesson 3: Probability Distribution 2

Lecture 26 Lesson 3: Probability Distribution 3

Lecture 27 Lesson 3: Probability Distribution 4

Lecture 28 Lesson 3: Probability Distribution 5

Lecture 29 Lesson 3: Probability Distribution 6

Section 4: Module 4: Data Mining

Lecture 30 Lesson 1: About Data Mining

Lecture 31 Lesson 1: About Data Mining 2

Lecture 32 Lesson 1: About Data Mining 3

Lecture 33 Lesson 1: About Data Mining 4

Lecture 34 Lesson 1: About Data Mining 5

Lecture 35 Lesson 2: Data Structure

Lecture 36 Lesson 2: Data Structure 2

Lecture 37 Lesson 2: Data Structure 3

Lecture 38 Lesson 2: Data Structure 4

Lecture 39 Lesson 2: Data Structure 5

Lecture 40 Lesson 2: Data Structure 6

Section 5: Module 5: Machine Learning

Lecture 41 Lesson 1: Machine Learning Techniques

Lecture 42 Lesson 1: Machine Learning Techniques 2

Lecture 43 Lesson 1: Machine Learning Techniques 3

Lecture 44 Lesson 1: Machine Learning Techniques 4

Lecture 45 Lesson 1: Machine Learning Techniques 5

Lecture 46 Lesson 1: Machine Learning Techniques 6

Lecture 47 Lesson 1: Machine Learning Techniques 7

Lecture 48 Lesson 1: Machine Learning Techniques 8

Lecture 49 Lesson 1: Machine Learning Techniques 9

Lecture 50 Lesson 1: Machine Learning Techniques 10

Lecture 51 Lesson 1: Machine Learning Techniques 11

Lecture 52 Lesson 2: Other Methods

Lecture 53 Lesson 2: Other Methods 2

Lecture 54 Lesson 2: Other Methods 3

Lecture 55 Lesson 2: Other Methods 4

Section 6: Module 6: Tools and Function and Assignment

Lecture 56 Lesson 1: Business Intelligent Tools

Lecture 57 Assignment: Data Science

Professionals with a background or interest in Data Science who aspire to deepen their expertise and establish themselves as leaders in the field,,Aspiring professionals and early-career learners aiming to build strong foundations in Data Science alongside critical skills such as Communication