Data Analytics Career Overview – From Skills to Interviews

Data Analytics Career Overview – From Skills to Interviews, available at $39.99, has an average rating of 5, with 72 lectures, based on 59 reviews, and has 1071 subscribers.

You will learn about If analytics career is right for you What tools and skills you need for getting an analytics job To know how analytics can help you in work Understand different analytics tools and use cases Frequently seen mistakes during analysis Quantitative method that can help you better in analysis Interview questions for analytics roles Some tips of job offers negotiation This course is ideal for individuals who are People who are interested in but new to analytics or Analytics workers who are struggle to find better methodologies for analysis or Engineers or Product Managers who would like to know more about product analysis or People from marketing or operation who would like to apply analytics in their work It is particularly useful for People who are interested in but new to analytics or Analytics workers who are struggle to find better methodologies for analysis or Engineers or Product Managers who would like to know more about product analysis or People from marketing or operation who would like to apply analytics in their work.

Enroll now: Data Analytics Career Overview – From Skills to Interviews

Summary

Title: Data Analytics Career Overview – From Skills to Interviews

Price: $39.99

Average Rating: 5

Number of Lectures: 72

Number of Published Lectures: 72

Number of Curriculum Items: 72

Number of Published Curriculum Objects: 72

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

If analytics career is right for you
What tools and skills you need for getting an analytics job
To know how analytics can help you in work
Understand different analytics tools and use cases
Frequently seen mistakes during analysis
Quantitative method that can help you better in analysis
Interview questions for analytics roles
Some tips of job offers negotiation

Who Should Attend

People who are interested in but new to analytics
Analytics workers who are struggle to find better methodologies for analysis
Engineers or Product Managers who would like to know more about product analysis
People from marketing or operation who would like to apply analytics in their work

Target Audiences

People who are interested in but new to analytics
Analytics workers who are struggle to find better methodologies for analysis
Engineers or Product Managers who would like to know more about product analysis
People from marketing or operation who would like to apply analytics in their work

Do you need data analysis in your work? Or you want to be an data analyst but don’t know where to start? Many companies claim that they are data-driven and looking for analytical talents. But what is data driven and what exactly is analytical skills? Why we are already looking at numbers but still don’t know what to do? Why I have required skills like SQL or Python, but still not hired?

If you have related questions like mentioned above or wonder if analytics career is right for you, this course could be right to you. This course won’t teach you everything of SQL, Python, or R. But will let you know what tools or techniques you need to be an analyst. It’s perfect for students or people who want to be analyst.

I’ll walk you through what roles you would have chance to apply analysis, what popular tools there are in tech industry, and how the interviews would look like. I even provided the list of courses and resources that I recommend. My 5 years of experience and job searching knowledge sharing in a nutshell.

Chapters

Overview

Different roles and their scenarios of using analysis during works

Tools

Spreadsheet
SQL
Tableau
Python & R

Quantitative Analysis

Metrics Definition
Normalization
Frequently seen mistakes

Interview

Behavioral
SQL
Technical Screening
Case Interviews

Course Curriculum

Chapter 1: Course Overview

Lecture 1: Intro to the course

Lecture 2: Course Materials

Chapter 2: Analytics Roles Overview

Lecture 1: Analytics Roles Breakdown

Lecture 2: Marketing Analyst Scenario – Allocating Budget

Lecture 3: Operation Team Scenario – Customer Service Analysis

Lecture 4: People Analysis Scenario – Finding Potential Attrition

Lecture 5: Business Analyst Scenario – Building Reports & Dashboards

Lecture 6: Product Analyst Scenario – Improving E-commerce Website

Lecture 7: Data Scientist Scenario – Marketing Customer Segmentation

Chapter 3: Analytics Tools

Lecture 1: Analytics Tools Overview

Lecture 2: Why is SQL important?

Lecture 3: SQL – Select & From

Lecture 4: SQL – Where

Lecture 5: SQL – Group By & Aggregation

Lecture 6: SQL – Order BY

Lecture 7: SQL – Join & ERD

Lecture 8: SQL – 4 Types of Join

Lecture 9: SQL – Self Join

Lecture 10: SQL – Window Functions

Lecture 11: SQL – Self-Learning & Portfolio Building

Lecture 12: Tableau – Business Intelligence & Visualization

Lecture 13: Python/R – Why I need to learn Python/R

Lecture 14: Python/R – Example 1

Lecture 15: Python/R – Example 2

Lecture 16: Python/R – Resources List

Chapter 4: Quantitative Analysis

Lecture 1: Intro

Lecture 2: Product Sense – Metrics Definition

Lecture 3: Metrics Definition – Question 1

Lecture 4: Metrics Definition – Answer 1

Lecture 5: Metrics Definition – Question 2

Lecture 6: Metrics Definition – Answer 2

Lecture 7: Normalization

Lecture 8: Normalization – Question 1

Lecture 9: Normalization – Answer 1

Lecture 10: Normalization – Question 2

Lecture 11: Normalization – Answer 2

Lecture 12: Product Case Practice – Question

Lecture 13: Product Case Practice – Answer

Lecture 14: Recommended Resources

Chapter 5: Interviews

Lecture 1: Intro & Agenda

Lecture 2: Self Introduction

Lecture 3: Behavior Questions

Lecture 4: Technical Questions – Intro

Lecture 5: SQL – Question 1

Lecture 6: SQL – Solution 1

Lecture 7: SQL – Question 2

Lecture 8: SQL – Solution 2

Lecture 9: Technical Questions – Data Manipulations

Lecture 10: Technical Questions – Algorithms

Lecture 11: Case Interview – Intro

Lecture 12: Case Interview – Question 1

Lecture 13: Case Interview – Solution 1

Lecture 14: Case Interview – Question 2

Lecture 15: Case Interview – Solution 2

Lecture 16: Case Interview – Question 3

Lecture 17: Case Interview – Solution 3

Lecture 18: Case Interview – Deal with Ambiguity – Question 1

Lecture 19: Case Interview – Deal with Ambiguity – Solution 1

Lecture 20: Case Interview – Deal with Ambiguity – Question 2

Lecture 21: Case Interview – Deal with Ambiguity – Solution 2

Lecture 22: Case Interview – Advanced Analytics Intro

Lecture 23: Analytics Case Interview – Question 1

Lecture 24: Analytics Case Interview – Solution 1

Lecture 25: Analytics Case Interview – Question 2

Lecture 26: Analytics Case Interview – Solution 2

Lecture 27: Case Interview – Thinking Process

Lecture 28: Case Interview – Thinking Process Example 1

Lecture 29: Case Interview – Thinking Process Example 2

Lecture 30: Interview Resources

Lecture 31: Offer Negotiation

Lecture 32: Congratulations!

Lecture 33: What's Next

Instructors

WeiChun (Marcus) Chang
Product Data Scientist

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 2 votes
4 stars: 3 votes
5 stars: 54 votes

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