Cole Dwiggins’ Post

You're probably wondering why you can't get your first data job. Well, I hate to be the bearer of bad news, but it's not because you don't know Python, SQL, or R. It's because you don't have any political background. I'm kidding (mostly). But, in all seriousness, it's important to remember that getting a data job isn't just about technical skills. It's also about being a good fit for the company culture and requirements. So, maybe instead of learning another programming language, try finding some recommendations i.e. NETWORKING, and try to prove yourself for the role that you are applying for.

Sometimes systems just try to make you fail. If you press the switch. The one that turns on your green ring. The one that is intended to help you in your job search The one that shows you are “Open to Work” It’ll end up offering you mediocrity. It’ll suggest you post poorly worded drivel. It’ll suggest to you the same as it suggested to Richard and Fred So stop. You work in Data. You deal in numbers and facts. Be specific. You aren’t just Open to Work You are looking for X (or Y) based in A (or B) You’ve got these key skills (not just Python or SQL, that’s a given) You’ve delivered these successes (think 12345 or £££ made or saved) You can do better than the automation. If you’re struggling with it ping me a message and I’ll help. #OpenToWork #Recruitment #NewJob #DataScience #DataEngineering #Algorithm

Python is the most preferred language for any person getting into the Data domain. Because it’s easy to learn, and beginner friendly. So, if you’re someone who wants to learn this language, take the help of this GIF and start your learning journey into the Data world. If you’re looking for a complete program for Data Analyst / Scientist, learn from Industry experts at Bosscoder Academy. Check them here: https://bit.ly/3W290sj Enroll with them and get access to → ✅ Structured curriculum to learn Python, SQL, Data Visualization and more. ✅ Personal Guidance from Data Analysts / Scientists working in Google, Samsung etc. ✅ Multiple projects for SQL, Python, etc. to build portfolio. P.S By the end of their program you won't need anything else to crack data analyst interviews. #python #datascience #jobs

💎 Streak: 78 Days 🎯 💡Solved the problem: Max Distance Between Same Elements Given an array arr[] with repeated elements, the task is to find the maximum distance between two occurrences of an element. 💭💭💭 Today, I realized how my problem-solving skills are evolving. I instinctively chose the right data structure—a dictionary—to solve the problem in O(n) time! 📈 Before, I might have approached it by iterating and creating temporary arrays, which would have been more complex and less efficient. But now, I’m tackling problems with clarity and precision, It all because of consistent practice. 💪 💯 Result: Solved with 100% accuracy! 🎉 🙏Thank you, everyone, for your support! #Placement #Python #DataStructures #MNC #Hiring #LinkedIn #JobSearch #Recruiters #GeeksforGeeks #DSA #DdhruvArora

Common spark interview questions that might help for cracking interviews as well as for better learning!! Picking up another topic from the comment section of one of my earlier posts related to spark i.e. : Garbage collection in Spark: Spark runs on the Java Virtual Machine (JVM). Because Spark can store large amounts of data in memory, it has a major reliance on Java’s memory management and garbage collection (GC). Therefore, garbage collection (GC) can be a major issue that can affect many Spark applications. Common symptoms of excessive GC in Spark are: 1. Application speed. 2. Executor heartbeat timeout. 3. GC overhead limit exceeded the error It’s easy to diagnose if your Spark application is suffering from a GC problem. The Spark UI marks executors in red if they have spent too much time doing GC. Some of the basic things we can do to try to address GC issues: a. Data Structures : If using RDD-based applications, use data structures with fewer objects. For example, use an array instead of a list. b. Storing Data Off-Heap : The Spark execution engine and Spark storage can both store data off-heap. You can switch on off-heap storage using the following commands: –conf spark.memory.offHeap.enabled = true –conf spark.memory.offHeap.size = Xgb. Be careful when using off-heap storage as it does not impact on-heap memory size, i.e. it won’t shrink heap memory. So, to define an overall memory limit, assign a smaller heap size. c. Built-in vs. User Defined Functions (UDFs) : If you are using Spark SQL, try to use the built-in functions as much as possible, rather than writing new UDFs. Most of the Spark UDFs can work on UnsafeRow and don’t need to convert to wrapper data types. This avoids creating garbage, also it plays well with code generation. d. Be Stingy About Object Creation : Remember we may be working with billions of rows. If we create even a small temporary object with 100-byte size for each row, it will create 1 billion * 100 bytes of garbage. #pyspark #interviewpreparation

I am thrilled to announce that I have earned the Python Development Associate certification! 🐍💻 I am passionate about leveraging technology to solve real-world problems and continuously learning in this dynamic field. I am actively seeking opportunities as a Python Developer, Software Engineer, Data Analyst, or Web Developer. Let’s connect to explore how I can contribute to your team’s success! #Python #Certification #ProfessionalDevelopment #Tech #Programming #ContinuousLearning #CareerGrowth

Arun Prakash M makes a brilliant point here. Adding on to it on why I think python is a good language to learn. 1. range of use: you can use python for - data science (tons of models are python based) - data engineering (date pipelines pretty much run on python) - devops (almost all infrastructure as code platforms have a python API) - backend. (Django, FastAPI) there are even efforts to make python write frontend code. 2. easy to learn: compared to most languages, python is extremely easy to learn. 3. job opportunities: python has become the de facto in most job requirements. learning it will give you an added advantage. Happy learning!

Python or C++: Which Should You Choose for Your Career? Choosing between Python and C++ can be crucial for your career path. For instance, C++ is the go-to language used in high-frequency trading. Moving data in micro- and nanoseconds is critical in this field, and the performance and speed of C++ make it indispensable. If you are aiming for a career with this kind of high-performance-oriented job, C++ is enough, not even Python or Java. Python, on the other hand, is the preferred language in data science.   Its simplicity, extensive libraries, and strong community support make it ideal for tasks involving data analysis, machine learning, and AI. So people, if you are going for a data science job in data science or analytics, Python will serve you well and is the best choice.

🌟 Why Python is Essential for Data Analysis & Career Growth 🌟 Python has become a must-have skill in data analysis, and for good reason! Here’s why it can power your career forward: 📈 *Efficiency*: Python's vast libraries—like Pandas, NumPy, and Matplotlib—make data manipulation, visualization, and analysis a breeze. It handles complex tasks in just a few lines of code. 💡 User-Friendly: With its readable syntax and active community, Python is beginner-friendly while still offering advanced functionality. This means you can quickly move from learning to implementing real-world solutions. 🌐 In-Demand Skill: From startups to tech giants, companies seek data professionals who can leverage Python to unlock data-driven insights. It’s a skill that truly stands out on your resume. 🚀 Growth Opportunities: Python opens doors to advanced fields like Machine Learning, Artificial Intelligence, and Big Data—further boosting your career prospects! If you’re looking to break into data analysis or supercharge your data career, Python is the tool you need! 🐍💼 #Python #DataAnalysis #CareerGrowth #DataScience #MachineLearning