Data Analytics Career Overview - From Skills To Interviews

A guide to your career in analytics track

What you'll 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

Requirements

No prerequisite for coding and analysis, but requires basic statistics knowledge, i.e. mean, percentiles, and distribution

Ideally some SQL & spreadsheet knowledge would be better

Description

Do you need analysis in your work? Or you want to be an 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. ChaptersOverviewDifferent roles and their scenarios of using analysis during workToolsSpreadsheetSQLTableauPython & RQuantitative AnalysisMetrics DefinitionNormalizationFrequently seen mistakesInterviewBehavioralSQLTechnical ScreeningCase Interviews

Overview

Section 1: Course Overview

Lecture 1 Intro to the course

Lecture 2 Course Materials

Section 2: Analytics Roles Overview

Lecture 3 Analytics Roles Breakdown

Lecture 4 Marketing Analyst Scenario - Allocating Budget

Lecture 5 Operation Team Scenario - Customer Service Analysis

Lecture 6 People Analysis Scenario - Finding Potential Attrition

Lecture 7 Business Analyst Scenario - Building Reports & Dashboards

Lecture 8 Product Analyst Scenario - Improving E-commerce Website

Lecture 9 Data Scientist Scenario - Marketing Customer Segmentation

Section 3: Analytics Tools

Lecture 10 Analytics Tools Overview

Lecture 11 Why is SQL important?

Lecture 12 SQL - Select & From

Lecture 13 SQL - Where

Lecture 14 SQL - Group By & Aggregation

Lecture 15 SQL - Order BY

Lecture 16 SQL - Join & ERD

Lecture 17 SQL - 4 Types of Join

Lecture 18 SQL - Self Join

Lecture 19 SQL - Window Functions

Lecture 20 SQL - Self-Learning & Portfolio Building

Lecture 21 Tableau - Business Intelligence & Visualization

Lecture 22 Python/R - Why I need to learn Python/R

Lecture 23 Python/R - Example 1

Lecture 24 Python/R - Example 2

Lecture 25 Python/R - Resources List

Section 4: Quantitative Analysis

Lecture 26 Intro

Lecture 27 Product Sense - Metrics Definition

Lecture 28 Metrics Definition - Question 1

Lecture 29 Metrics Definition - Answer 1

Lecture 30 Metrics Definition - Question 2

Lecture 31 Metrics Definition - Answer 2

Lecture 32 Normalization

Lecture 33 Normalization - Question 1

Lecture 34 Normalization - Answer 1

Lecture 35 Normalization - Question 2

Lecture 36 Normalization - Answer 2

Lecture 37 Product Case Practice - Question

Lecture 38 Product Case Practice - Answer

Lecture 39 Recommended Resources

Section 5: Interviews

Lecture 40 Intro & Agenda

Lecture 41 Self Introduction

Lecture 42 Behavior Questions

Lecture 43 Technical Questions - Intro

Lecture 44 SQL - Question 1

Lecture 45 SQL - Solution 1

Lecture 46 SQL - Question 2

Lecture 47 SQL - Solution 2

Lecture 48 Technical Questions - Data Manipulations

Lecture 49 Technical Questions - Algorithms

Lecture 50 Case Interview - Intro

Lecture 51 Case Interview - Question 1

Lecture 52 Case Interview - Solution 1

Lecture 53 Case Interview - Question 2

Lecture 54 Case Interview - Solution 2

Lecture 55 Case Interview - Question 3

Lecture 56 Case Interview - Solution 3

Lecture 57 Case Interview - Deal with Ambiguity - Question 1

Lecture 58 Case Interview - Deal with Ambiguity - Solution 1

Lecture 59 Case Interview - Deal with Ambiguity - Question 2

Lecture 60 Case Interview - Deal with Ambiguity - Solution 2

Lecture 61 Case Interview - Advanced Analytics Intro

Lecture 62 Analytics Case Interview - Question 1

Lecture 63 Analytics Case Interview - Solution 1

Lecture 64 Analytics Case Interview - Question 2

Lecture 65 Analytics Case Interview - Solution 2

Lecture 66 Case Interview - Thinking Process Example 1

Lecture 67 Case Interview - Thinking Process Example 2

Lecture 68 Case Interview - Thinking Process

Lecture 69 Interview Resources

Lecture 70 Offer Negotiation

Lecture 71 Congratulations!

Lecture 72 What's Next

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