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Schedule Interview NowAt Softaims, I’ve found a workplace that thrives on collaboration and purposeful creation. The work we do here is about more than technology—it’s about transforming ideas into results that matter. Every project brings a mix of challenges and opportunities, and I approach them with a mindset of continuous learning and improvement. My philosophy centers around three principles: clarity, sustainability, and impact. Clarity means designing systems that are understandable, adaptable, and easy to maintain. Sustainability is about building with the future in mind, ensuring that the work we do today can evolve gracefully over time. And impact means creating something that genuinely improves how people work, connect, or experience the world. One of the most rewarding aspects of working at Softaims is the diversity of thought that every team member brings. We share insights, question assumptions, and push each other to think differently. It’s this culture of curiosity and openness that drives the quality of what we produce. Every solution we deliver is a reflection of that shared dedication. I’m proud to contribute to projects that not only meet client expectations but also exceed them through thoughtful execution and attention to detail. As I continue to grow in this journey, I remain focused on delivering meaningful outcomes that align technology with purpose.
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As part of our data analytics initiative, I've crafted a comprehensive Looker dashboard tailored specifically for our company's orders. This intuitive and dynamic tool provides an in-depth, real-time view of our order data, empowering us to make informed decisions and gain valuable insights. Upon accessing the dashboard, you'll immediately notice its user-friendly interface, thoughtfully designed to cater to both beginners and seasoned analysts. The main page presents a high-level overview, showcasing key performance indicators such as order volume, revenue, and fulfillment status at a glance. Navigating through the various tabs, you'll find detailed breakdowns of order metrics, allowing for a granular analysis of our sales performance. The "Top Products" section highlights our best-selling items, aiding in inventory planning and marketing strategies. For a customer-centric perspective, the "Customer Segmentation" tab categorizes our client base, helping us understand their preferences and behavior.
Machine Learning in Python refers to the application of various algorithms and statistical models to enable computers to learn from data and improve their performance on a specific task without being explicitly programmed. Python has become one of the most popular programming languages for machine learning due to its simplicity, versatility, and a rich ecosystem of libraries and tools specifically designed for data manipulation, analysis, and modeling. Key components of Machine Learning in Python: Libraries: Python boasts powerful libraries that simplify the implementation of machine learning algorithms. Some of the most prominent ones include: Scikit-learn: A comprehensive library that offers a wide range of supervised and unsupervised learning algorithms, data preprocessing tools, and model evaluation functions. TensorFlow and Keras: Libraries primarily used for deep learning tasks, enabling the construction and training of complex neural networks. PyTorch: Another popular deep learning library known for its dynamic computation graphs and ease of use. Data Preprocessing: Before feeding data into a machine learning model, it often requires preprocessing. Python provides various libraries for data manipulation and cleaning, such as Pandas for tabular data and NumPy for numerical operations. Feature Extraction: Python facilitates extracting relevant features from raw data to represent it in a more suitable format for machine learning models. Libraries like Scikit-learn and NumPy offer functions for feature selection and extraction. Model Training: Python enables training machine learning models by implementing algorithms provided by libraries like Scikit-learn or customizing neural network architectures using TensorFlow or PyTorch. The model is fed with training data to learn patterns and relationships within the data. Model Evaluation: Python allows for evaluating the model's performance using metrics like accuracy, precision, recall, F1-score, and more. Scikit-learn provides a wide range of evaluation functions to assess model performance. Model Deployment: After training and evaluation, Python can be used to deploy the trained machine learning models into production systems, allowing them to make predictions on new, unseen data. Benefits of using Python for Machine Learning: Ease of learning and readability: Python's simple syntax and readable code make it accessible for both beginners and experienced developers. Extensive libraries and frameworks: Python has a vast collection of libraries tailored for data manipulation, visualization, and machine learning, making development faster and more efficient. Strong community support: Python's popularity in the data science and machine learning community means that there are abundant resources, tutorials, and documentation available online. Integration with other technologies: Python can be easily integrated with other technologies, making it a versatile choice for building end-to-end machine learning solutions. Overall, Python has become the language of choice for many data scientists and machine learning practitioners due to its powerful ecosystem, ease of use, and rapid prototyping capabilities.
This Python course will not only take your programming skills to the next level, but also give you a problem-solving superpower! In it, you will develop a thorough understanding of Python and its programming capabilities, as well as hone your computational thinking. Moreover, you will learn how to implement object-oriented programming (OOP), create Python charts in Matplotlib, and work with different IDEs like Spyder and Jupyter. As you progress, you’ll get to practice your skills with fun and challenging exercises like solving the Sierpinski Triangle and the Towers of Hanoi. Your instructor, Giles McMullen-Klein, is an Oxford-educated programmer who made proficient use of Python during his time at the University. Not only that but he is motivating, enthusiastic, and truly passionate about the programming language he teaches! Data analysis is a process of inspecting, cleansing, transforming, and modeling data to discover useful information, informing conclusions, and support decision-making. Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. Python is dynamically-typed and garbage-collected. It supports multiple programming paradigms, including structured, object-oriented and functional programming
An HR dashboard is a business intelligence tool that allows Human Resource teams to track, analyze and report on HR KPIs. Modern, interactive dashboards leverage an HR analytics platform which makes it easy to combine data from all systems and to deeply explore this data directly within the dashboard. Main points which I have covered in this HR Dashboard. 1)Recruitment & selection. 2)Performance management. 3)Learning & development. 4)Succession planning. 5)Compensation and benefits. 6)Human Resources Information Systems. 7)HR data and analytics. Additional: Microsoft Excel Python Programming Business Intelligence Data Visualization Microsoft Power BI Data Analyst Power Bi Dashboard Dashboard Design Powerbi Excel Dashboard .netazure devops microsoft azure spark azure cloud aws devops BI Reports Design Data Analysis
What do companies in e-commerce, entertainment, healthcare, manufacturing, marketing, finance, tech, and hundreds of other industries all have in common? You guessed it, they all use data. Organizations of all kinds need data analysts to help them improve their processes, identify opportunities and trends, launch new products, provide great customer service, and make thoughtful decisions. Hi, I'm Tony, a program manager at Google and a data analyst myself. I like to welcome you to the Google Data Analytics Certificate. Now, there are lots of great reasons to earn this certificate. Maybe you're thinking about starting a career in the exciting world of data analytics, or maybe you're just fascinated by the power of data as I am. No matter what brought you here, you're in the right place to kick-start a career and learn industry-relevant skills in data analytics. But first, what exactly is data? Well, I'll like to say that data is a collection of facts. This collection can include numbers, pictures, videos, words, measurements, observations, and more. Once you have data, analytics puts it to work through analysis. Data analysis is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making. And it doesn't stop there. Data evolves over time which means this analysis or analytics, as we call it, can give us new information throughout data's entire life cycle. Data is everywhere. You use and create data everyday. Have you ever read reviews of a product before deciding whether or not to buy it? That's data analysis. Or maybe you wear a fitness tracker to count your steps so you can stay active throughout the day. That's data analysis. But you don't just use data. You also create huge amounts of it every single day. Any time you use your phone, look up something online, stream music, shop with a credit card, post on social media or use GPS to map a route, you're creating data. Our digital world and the millions of smart devices inside of it have made the amount of data available truly mind-blowing. Google process more than 40,000 searches every second. That's 3.5 billion searches a day and 1.2 trillion searches every year. Here's another way to think about it. YouTube has almost two billion users. If YouTube users made up a country, it would be the largest in the world. All of that data is transforming the world around us. The publication The Economist recently called data the world's most valuable resource. It's easy to see why data analysts are so valued by their organizations. What exactly does a data analyst do? Put simply, a data analyst is someone who collects, transforms, and organizes data in order to help make informed decisions. Besides the role itself, one of the most exciting parts of being a data analyst is the number of opportunities available. The demand for data analysts is greater than the number of qualified people to fill these job openings. This certificate program is a great first step in your journey to finding a job you love. Data analysts come from many different backgrounds and have all kinds of life experiences. You don't need decades of work experience or an expensive education to get started. Many data analysts taught themselves the skills they needed to land their first job, just like you're doing right now. Now let's talk more about what you're going to learn. The Google Data Analytics Certificate is split into courses based on different processes for data analysis.
Bachelor of Accountancy (BAcc) in Computational Finance (Applied Computer Science)
2019-01-01-2023-01-01
High school degree in ICS
2017-01-01-2019-01-01
Other in Career Prepration
2021-01-01-2021-01-01
Master's degree in Computer science