Getting your first job in Data Analytics
Data analytics has become a crucial field driving decision-making and shaping the future of Artificial Intelligence. If you’re interested in landing your first job in data you need to learn what you need to be successful. We’ll cover the educational background, essential skills, and experience you'll need. Plus, we'll talk about whether you need a strong foundation in mathematics, statistics, and programming, as well as industry tools likeSQL, Python, Power BI, and Tableau.
Jess Ramos is a Senior Data Analyst with a background in Mathematics, and a Master's degree in Business Analytics, Jess has inspired thousands of professionals towards their first job. Her practical insights and passion for data-driven decision-making make her an invaluable resource for those looking to enter this field.
Links
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A Journey into Data Analytics: https://go.raybo.org/TFIT2601
Insights into Coding & SQL Mistakes: https://go.raybo.org/TFIT2602
Introduction to Chatbot Plugins: https://go.raybo.org/TFIT2603
Chatbot Misuse in Corporate Worlds: https://go.raybo.org/TFIT2604
Legal Pitfalls in Chatbot Utilization: https://go.raybo.org/TFIT2605
Profile of a Data Professional: https://go.raybo.org/TFIT2606
Index
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00:00 Introduction
00:49 Getting into the Field
01:37 Importance of Internships
02:32 Technical and Non-Technical Aspects
03:29 Defining Data Analysis
04:12 How Businesses Use Data Analysts
05:36 Types of Analytics
07:43 Handling Uncertain Data
09:35 Skills Needed for Data Analysts
12:50 SQL Data Model
13:39 Problem-Solving in Data Analytics
14:54 Soft Skills for Data Analytics
17:10 Becoming a Data Analyst
18:36 Transitioning into Data Analytics
19:50 Building a Portfolio for Data Analysts
20:18 Data Analyst's Portfolio
22:00 Stand Out Portfolios
23:22 Memorable Portfolio Projects
24:26 Relevant Industry Projects
25:38 Learning from Interview Mistakes
26:44 Embracing Imperfection in Coding
28:23 Impact of AI in Data Jobs
29:47 Concerns with AI Tools
30:31 Balancing Data with Expertise
33:59 Limitations of AI in Analysis
36:07 AI in SQL Queries and Visualization
36:25 AI for Efficiency, Not Replacement
37:49 Lessons from Past Advancements
38:13 Caution with AI Tools
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