Remote WordPress Developer/Technical Writer Job at Soflyy

* Your main responsibility will be to write high-quality technical long-form content.
* Documentation, tutorials, blog posts, and more.
* You don't need to know anything about SEO, we will take care of that.
* All you need to do is be an expert in WordPress and write engaging content.

Apply Here:
https://kenyatrends.co.ke/remote-wordpress-developer-technical-writer-job-at-soflyy/

Remote Golang Engineer Job at Canonical
Location: Remote
* Design and implement well-tested and documented software in Go
Apply Here:
https://kenyatrends.co.ke/remote-golang-engineer-job-at-canonical/

IDEAL CV FORMAT GUIDELINE

1. Header (Contact Information)
Full Name
Location: City, State, Country
Email: Professional Email Address
Phone Number
LinkedIn Profile (if available)

2. Professional Summary (Elevator Pitch)
A 3-4 sentence summary highlighting years of experience, key skills, and measurable achievements.

3. Key Skills (ATS-Optimized & Categorized)
Example:
Customer Service & Support: Query Resolution, Complaint Handling, Retention Strategies
Software Proficiency: Banking Software (Finacle, Flexcube), CRM Systems, MS Office Suite
Administrative & Analytical Skills: Report Writing, Data Analysis, Documentation

4. Professional Experience (Reverse Chronological Order)
Job Title | Company Name | Location | Dates of Employment
Key Achievements:
* Use bullet points (4-5 per role)
* Highlight measurable achievements
* Incorporate industry-specific keywords

5. Education
Degree | Institution | Location | Year of Completion (or "In View" if ongoing)

6. Certifications & Training (If Applicable)
Customer Service Training
Banking & Financial Services Certifications
CRM & Banking Software Training

7. Additional Information (Optional)
Languages Spoken (if relevant)
Professional Membership

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🔰Edge Computing Master the Next Frontier of Computing

Language : English

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📈 Head of Marketing
👩🏻‍💻 Backend Engineer
🤝 Talent Acquisition
🎨 Brand Designer

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- Remote-first, work from where you want
- Async-first, you control your calendar
- Ambition and mastery are two of our four core values, we strive to do everything at a world-class level
- Bi-annual in-person team retreats in incredible locations
- Competitive total compensation package, including base salary, bonuses, equity, and various work/wellness/education perks
- 40 vacation days + 12 health days per year

Apply Here:
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*Short roadmap to learn Tableau 😄👇*

1. Getting Started:
- Download and install Tableau Public (free) or Tableau Desktop (trial version).
- Explore the Tableau interface to get familiar with its components.

2. Data Connection:
- Learn to connect Tableau to your data sources like Excel, CSV, databases, or cloud services.

3. Data Preparation:
- Understand how to clean and shape data in Tableau using the Data Source tab.

4. Basic Visualization:
- Create simple visualizations like bar charts, line charts, and scatter plots.

5. Calculations:
- Learn about calculated fields and basic functions for more complex data transformations.

6. Dashboards and Stories:
- Explore creating interactive dashboards and stories to present your insights effectively.

7. Advanced Visualizations:
- Dive into more advanced charts and graphs, such as heat maps, treemaps, and dual-axis charts.

8. Advanced Calculations:
- Master advanced calculations, such as level of detail (LOD) expressions and table calculations.

9. Mapping:
- Learn how to create maps and geospatial visualizations using Tableau's mapping features.

10. Data Blending:
- Understand how to blend data from multiple sources for comprehensive analysis.

11. Performance Optimization:
- Optimize the performance of your Tableau workbooks for larger datasets.

12. Tableau Server (Optional):
- If needed, explore Tableau Server for collaboration and sharing.

13. Online Resources:
- Utilize online tutorials, documentation, and forums to expand your knowledge.

14. Practice:
- Work on real-world projects to apply what you've learned. Remember to practice and apply your knowledge as you progress through each stage.

15. Certification (Optional):
- Consider pursuing Tableau certification for formal recognition of your skills.

Data Analytics Resources: https://t.me/dataanalysisresourcestp

Hope it helps :)

WhatsApp Channel:
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Life of a Data Engineer.....

Business user : Can we add a filter on this dashboard. This will help us track a critical metric.
me : sure this should be a quick one.

Next day :

I quickly opened the dashboard to find the column in the existing dashboard's data sources.  -- column not found

Spent a couple of hours to identify the data source and how to bring the column into the existence data pipeline which feeds the dashboard( table granularity , join condition etc..).

Then comes the pipeline changes , data model changes , dashboard changes , validation/testing.

Finally deploying to production and a simple email to the user that the filter has been added.

A small change in the front end but a lot of work in the backend to bring that column to life.

Never underestimate data engineers and data pipelines 💪

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Breaking into Data Analysis can be very confusing in 2025!

Should I learn SQL or NoSQL? Tableau or Power BI? Excel or Google Sheets? Python or R?

Fundamental principles are more important than tools:

Understanding data cleaning and preprocessing is more important than SQL vs NoSQL.

Understanding data visualization concepts is more important than Tableau vs Power BI.

Understanding statistical analysis is more important than Excel vs R.

Understanding programming for data manipulation is more important than Python vs R.

Knowing these will allow you to pick up new emerging tools easily.

Stick to fundamentals first.

Best Top-Notch Data Analytics Resources 👇👇
https://t.me/dataanalysisresourcestp

Hope this helps you 😊

31 Essential Functions to Supercharge Your Data Analysis

Read Here:
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Python AI Roadmap

Stage 1 – Learn Python Basics (Syntax, Data Types)
Stage 2 – Data Handling (Pandas, NumPy)
Stage 3 – Machine Learning (Scikit-Learn, Basic Models)
Stage 4 – Deep Learning (TensorFlow/PyTorch, Neural Networks)
Stage 5 – Build & Train ML Models
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Complete Roadmap to learn Excel in 2025 👇👇

1. Basic Excel Skills:
   - Familiarize yourself with Excel's interface and navigation.
   - Learn basic formulas (SUM, AVERAGE, COUNT, etc.).
   - Understand cell referencing (absolute vs. relative).

2. Data Entry and Formatting:
   - Practice entering and formatting data efficiently.
   - Explore cell formatting options for a clean and organized dataset.

3. Advanced Formulas:
   - Master more advanced formulas like VLOOKUP, HLOOKUP, INDEX-MATCH.
   - Learn logical functions (IF, AND, OR).
   - Understand array formulas for complex calculations.

4. Pivot Tables:
   - Gain proficiency in creating Pivot Tables for data summarization.
   - Learn to customize and format Pivot Tables effectively.

5. Data Cleaning:
   - Acquire skills in cleaning and transforming data.
   - Explore text-to-columns, remove duplicates, and data validation.

6. Charts and Graphs:
   - Learn to create various charts (bar, line, pie) for data visualization.
   - Understand chart formatting and customization.

7. Dashboard Creation:
   - Combine charts and tables to build basic dashboards.
   - Explore dynamic dashboards using Excel features.

8. Macros and VBA:
   - Dive into basic automation using Excel macros.
   - Learn Visual Basic for Applications (VBA) for more advanced automation.

9. Power Query:
   - Introduce yourself to Power Query for enhanced data manipulation.
   - Learn to import, transform, and load data efficiently.

10. Advanced Excel Techniques:
   - Explore advanced features like Goal Seek, Solver, and Scenario Manager.
   - Master the use of data tables for sensitivity analysis.

11. Real-world Projects:
   - Apply your skills to real-world projects or datasets.
   - Practice solving analytical problems using Excel.
Remember to practice consistently, as hands-on experience is crucial for mastering Excel. This roadmap will provide a solid foundation for your journey into data analysis using Excel.

5️⃣ Free resources to practice Excel

W3Schools Excel Tutorial

SimpliLearn Intruduction to Ms Excel

https://bit.ly/3PSorPT

http://learn.microsoft.com/en-gb/training/paths/modern-analytics/

https://t.me/dataanalysisresourcestp/35

https://excel-practice-online.com/

Join for more: https://t.me/TechPsyche

ENJOY LEARNING 👍👍

Want to become a Data Analyst?

Here’s a roadmap with essential skills, tools & concepts you’ll need to master:

1. Data Fundamentals

Statistics: Learn descriptive statistics (mean, median, mode), distributions, hypothesis testing, and correlation.

Probability: Understand basic probability theory, including conditional probability, Bayes’ theorem, and probability distributions.

2. Data Cleaning

Data Cleaning Techniques: Handling missing values, removing duplicates, and outlier detection.

Data Transformation: Data type conversions, feature engineering, and handling categorical variables.

Pandas: Master data manipulation with Pandas (merge, join, group, pivot).

3. Data Visualization (https://t.me/dataanalysisresourcestp)

Data Visualization Libraries: Master Matplotlib, Seaborn, or Plotly for Python-based visualizations.

Power BI / Tableau: Get hands-on with BI tools to create interactive dashboards and visual reports.

Design Principles: Learn best practices for designing clear, effective visualizations.

4. SQL for Data Analysis (https://t.me/sqlresourcestp)

Basic SQL: SELECT, WHERE, ORDER BY, GROUP BY, JOINs.

Advanced SQL: Window functions, Common Table Expressions (CTEs), subqueries.

Aggregation Functions: SUM, AVG, MIN, MAX, COUNT.

Data Cleaning with SQL: Filtering, transforming, and merging data in SQL databases.

5. Excel for Data Analysis (https://t.me/dataanalysisresourcestp)

Data Cleaning in Excel: Use functions like TRIM, CLEAN, SUBSTITUTE.

Advanced Functions: VLOOKUP, HLOOKUP, INDEX-MATCH, IF, SUMIF, COUNTIF.

Data Visualization in Excel: Create pivot tables, charts, and dashboards.

6. Programming for Data Analysis (Python or R) (http://t.me/pythonresourcestp)

Python: Learn data handling and manipulation with Pandas and NumPy.

R: Basic syntax, data manipulation with dplyr, and data visualization with ggplot2.

Data Analysis Libraries: Pandas, NumPy, SciPy for Python or Tidyverse for R.

7. Exploratory Data Analysis (EDA)

Pattern Recognition: Use EDA to identify patterns, trends, and correlations in data.

Visual EDA: Use pair plots, heatmaps, and distribution plots for insights.

Summary Statistics: Understand distributions, variance, and central tendencies of variables.

8. Business Acumen

Domain Knowledge: Understand the industry-specific metrics relevant to your target job (e.g., finance, marketing, e-commerce).

Data Storytelling: Learn to communicate findings clearly and effectively, connecting insights to business goals.

KPI Analysis: Identify and measure key performance indicators for informed decision-making.

9. Data Collection & Sourcing

APIs: Learn to pull data from APIs (e.g., REST APIs) using tools like Python’s Requests library.

Web Scraping: Use tools like BeautifulSoup and Scrapy (be mindful of ethics and legality).

Database Connections: Query databases and integrate SQL with Python or R for more extensive analyses.

10. Dashboarding and Reporting (https://t.me/dataanalysisresourcestp)

Power BI / Tableau: Master the basics of dashboard design, interactivity, and sharing insights with stakeholders.

Reporting Best Practices: Design reports that are clear, actionable, and easy for non-technical stakeholders to interpret.

11. Soft Skills

Communication: Clearly present data insights and recommendations to stakeholders.

Critical Thinking: Approach problems analytically to uncover insights.

Collaboration: Learn how to work effectively within cross-functional teams, especially with non-technical colleagues.

Top-notch Data Analytics Resources (https://topmate.io/learning_resources/1456762)

Power BI Interview Questions (https://dev.to/henryclapton/top-15-advanced-power-bi-interview-questions-2942)

Free Resources to learn Data Analytics (https://t.me/dataanalysisresourcestp/37)

Data Analyst Learning Plan (https://t.me/dataanalysisresourcestp/36)

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Like for more data analytics resources ❤️

ENJOY LEARNING👍👍

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