Kene Agbodike[PMP®, MBCS, CITP]

Last week, as a Data Analytics learner in Dot Campus Africa. I learnt about Open Source and how important it is to understand how it works. Contributing to Open Source project is a way to put yourself out there and showcase what you've learnt. I was amazed at the number of projects you can contribute to even as a beginner on GitHub! PS: As a contributor, you can do things like: translation, fix bugs and even create contents! Because I don't gatekeep, here's why you should start considering getting involved with open source 😉 👇 #opensource #learningdataanalysis #dataanalysis #linkedin #techcommunity #dotcampus

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Did you know you can create interactive dashboards in Excel?😳 I was just as surprised! During the first few weeks of the Blossom Academy Generation Ghana data analytics program, I discovered the incredible potential of Excel for building dynamic, interactive dashboards. It completely changed the way I view and work with data. Here’s a sneak peek at the interactive dashboard I created using Excel, with data on a 2016 fruit and vegetable sales. The experience has been exciting, insightful, and it’s incredible how much can be achieved with Excel. I will continue sharing more skills, tips, and learnings as I grow in my journey! #GenerationGhana #BlossomAxademy #DataAnalytics #Excel #ExcelDashboards #InteractiveDashboards #DataVisualization #DataInsights #DashboardDesign #LearningJourney #DataSkills #DataStorytelling #ExcelTips #AnalyticsInAction #GrowingInData

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Hey #LagosTUG, I was really impressed by the simplicity and approach of Aliu Adekola Data Visualization Industry Insights session during Bootcamp Week 3. Inspired by his design perspective, I decided to build upon it. Check it out here: https://bit.ly/4i8neQX

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My Week As a Data Scientist As you know I am currently engaged in various courses on different learning platforms even as I continue to teach at Utidia Apprenticeship Institute (UAI) for the Data Science Track. This week I taught my class how to build simply models using linear regression, it was well understood and we continue practice because I’m a huge fan of reinforcement learning. I have also recently discovered a new data science library which is the ‘Polars Library’, there’s really not much difference between both libraries for data science/Python but it’s also that Polars boasts of more speed and efficiency or runtime as is summarized in its query optimization, “One of the other cores of Polars’ performance is how it evaluates code. Pandas, by default, uses eager execution, carrying out operations in the order you’ve written them. In contrast, Polars has the ability to do both eager and lazy execution, where a query optimizer will evaluate all of the required operations and map out the most efficient way of executing the code. This can include, among other things, rewriting the execution order of operations or dropping redundant calculations.” While I cannot take a bet on the Polars library for now it’s difficult to discountenace what it’s bringing to the table hence I encourage everyone to get familiar with it also. See you next week as I continue to work on various models and it’s deployment. #Datascience #Polars #DatascienceLibrary

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Analytics Engineering with a purpose - Empowering SMEs Across Sub-Saharan Africa ✨ As an Analytics Engineer, I work at the intersection of data and business strategy, designing models, creating reliable analytics pipelines, and transforming raw data into useful data. In the broader sense, analytics engineering plays a crucial role in enabling SMEs to scale and thrive. By building accurate data models and ensuring high data quality, analytics engineers help businesses optimize everything from procurement to customer service, enabling better decision-making based on clear, data-driven insights. For SMEs in Sub-Saharan Africa, where unique challenges like infrastructure limitations and resource constraints exist, data can be transformative. Reliable analytics allow these businesses to identify opportunities for growth, streamline operations, and ultimately contribute to a more sustainable, resilient economy. To illustrate the impact of analytics in action, here’s a heat map showing website traffic by time of day. Visualizations like this highlight peak engagement times, helping businesses strategically plan their resources, optimize marketing efforts, and better align customer service hours. These data-driven insights are especially valuable for small businesses aiming to make the most of their resources and engage their audiences effectively. #AnalyticsEngineering #DataForGood #SMEempowerment #DataTransformation

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Level up your Business Analysis skills! Join Oluwaseun Fagbule, Product Reliability Engineer at viafor a Practical Masterclass on performing Gap Analysis as a Business Analyst. Dive into a real-life case study and gain valuable insights. See you at 8PM GMT+1! Click on the link  bit.ly/4aIayfl to secure your spot promptly. #10alytics #businessanalysis

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Navigating the minefield: Here are #KEY mistakes you should avoid in data science and how they can shape your approach. Join our next Cohort starting end of May 2024 to learn more about Data Analysis and Business Intelligence. Click on link to get started https://lnkd.in/dRYE-Tw6 #data #linkedin #datascience #todayondata #everythingondata #wogepeef

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On day 9 of the Analytics Extra Mentorship Programme, under the guidance of mentor Adewale Yusuf (MVP, MCSA, MCDA, MCT). I had the opportunity to dive into "Mastering DAX for Reporting in Power BI," a session facilitated by Emmanuel Bassey. We explored various aspects of DAX (Data Analysis Expressions), including its components, uses in Power BI, its foundational pillars, and key DAX functions. The session provided valuable insights into how DAX enhances reporting and data analysis in Power BI, empowering users to build more dynamic and efficient reports. #DataAnalytics #PowerBI #DAX

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it was great having this discussion with @ishango.ai

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It’s been an amazing few weeks exploring the world of data engineering under the mentorship of Oluwafemi Olawoyin MBCS. Just like other data professions, I found data engineering fascinating and worth my time and attention. While I’m not new to tools like Python and SQL, interacting with databases from a Python environment and performing basic ETL has been especially intriguing. For a glimpse into how I handle database management, data integration, and data processing techniques, click here: https://lnkd.in/d7mKfzEm. More is surely coming! Keep an eye out for my upcoming Data Challenge, where I'll use Python and PySpark to tackle complex data problems. #DataEngineering #LearningJourney

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“The more you dig in the more you will see” That’s what it is in this Data space. I have never been drilled on SQL like I have been drilled these past few days. Concerning SQL, I have learnt many things in just a few days. Thanks to Jacob Ajala for the drill. By mere seeing me today, One would have thought that I have ran mad. By the end of today, having been through all the challenges to ensure that my project on SQL was completed. I thought to myself: “SQL came for my life but mercy said No”. I have just completed a challenging project where I harnessed the power of SQL to extract meaningful data, used Excel for insightful visualizations, and crafted a compelling PowerPoint presentation. This experience sharpened my data analysis and presentation skills, making me more proficient in transforming raw data into actionable insights. Full details in my next post.  #DataScience #SQL #Excel #PowerPoint #ProfessionalGrowth

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It has been so full of experience✍️ on the learning journey. Machine Learning is actually very deep, I never thought Machine learning is more wide than analytics. Africa Agility Foundation has done alot of investment in thousands of girls this Cohort 7.0, of which I'm one.👏 Tutors and Tech support have been of great help on the learning curve. #AfricaAgility #20DaysChallengeWithAA #GIT20DaysChallenge #20DaysChallengeDSMLWithAA #DataAnalytics #DataScience #MachineLearning

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Day 2/25 days of the Women in Data Africa challenge. Today, still on SETS in Tableau, I learnt how to apply different types of sets like “fixed sets” where we set the rules ourselves manually, The “dynamic sets” where we give condition to tableau. Then the “full members” and “shared members” set. It was an interesting exercise applying different types of sets on the dataset and understanding how they work. #25daysofDatawithWiDA #25daysofconsistencywithWiDA

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Hey, tech queens! 👩🏿‍💻   Need help with algorithms and data structures? Check out this video!" Watch and learn a valuable trick from this insightful video. Video credit: techgalanita #AfricaAgility #GirlsInTech #TechBootcamp #Cohort7.0 #DataScience #AI #Techtips

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Today was very insightful and remindful of all we have learnt in data science and machine learning. I used to find data science hard or difficult until I met #AfricaAgility they changed my story and now I am enjoying every bit of it. Today's class was all about revision of all we learnt for the past three weeks, that was really thoughtful of them so as not to get us choke up😁 talking about the revision it is important to understand the basic yet important aspect of Data science. below are a few: To import files on JupyterLab, you can follow these steps: Open JupyterLab and navigate to the directory where you want to import the file. Click on the "Upload" button in the top-right corner of the file browser. Select the file you want to import and click "Open". The file will be uploaded to the current directory in JupyterLab. Now, let's discuss different data types in Python and their uses: Numeric types: int: used for whole numbers (e.g. x = 5) float: used for decimal numbers (e.g. x = 3.14) complex: used for complex numbers (e.g. x = 2 + 3j) Boolean type: bool: used for logical values (e.g. x = True or x = False) Sequence types: list: a mutable collection of elements (e.g. x = [1, 2, 3]) tuple: an immutable collection of elements (e.g. x = (1, 2, 3)) range: a sequence of numbers (e.g. x = range(1, 10)) Mapping type: dict: a collection of key-value pairs (e.g. x = {"name": "John", "age": 30}) Set types: set: an unordered collection of unique elements (e.g. x = {1, 2, 3}) frozenset: an immutable version of a set (e.g. x = frozenset({1, 2, 3})) Each data type has its own specific uses and properties, and choosing the right data type for a particular task can greatly improve the efficiency and readability of your code #GIT20DaysChallenge #AfricaAgility #GirlsinTech #programmers #Data scientists

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I've recently ASCENDED to a new strata in the cloud ☁️. Delighted to announce my achievement of the AWS Solutions Architect Associate certification. Immensely grateful for the insightful guidance and mentorship from Yinka Daramola, whose firsthand experience as a Solutions Architect has been invaluable. And TalentUp Africa for an incredible growth opportunity. Throughout this journey, I've gained expertise in: ✅ Implementing security best practices and controls to protect data, systems, and resources. ✅ Designing systems that can recover from failures and disaster. ✅ Designing systems that scale and have high throughput using autoscaling, serverless and CDN services to meet the demands of high traffic applications. ✅ Optimizing cost in the cloud using tools like AWS budget, Trusted Advisor to monitor cost, analyse cost and optimize spending in the cloud, implementing cost-saving measures when setting up systems. Literally went from being a 'sule' to a 'solomon' in no time 😊. https://lnkd.in/dwVgp8qV

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