#Decimal Point Analytics

Celebrating International Yoga Day at Decimal Point Analytics, Nashik To promote holistic well-being and mindfulness at the workplace, a rejuvenating yoga session was organized for the team. Led by Yoga Instructor Ms. Smita Shinde, the session included: Asanas ,Pranayama (breathing techniques) ,Meditation The initiative aimed to encourage balance between body and mind — a reminder that health is not just physical, but mental and emotional too. The session was further enriched by a valuable talk on stress management at work by Dr. Ninad Thorat, HOD & Consultant Neurologist at KIMS Manavata Hospital, empowering the team with practical tools to stay mentally strong and focused in a demanding work environment. . . . #yoga #yogaday #yogasessions #stress #stressmanagement #nashik #kimsmanavatahospital

Are you passionate about data analytics and AI? Looking for a career opportunity that combines cutting-edge technology with a vibrant work culture? Then join me in exploring Decimal Point Analytics, located in the heart of Nashik! Their dynamic team attracts top talent from all over the country, and they offer an exciting work environment that’s perfect for anyone who loves innovation and problem-solving. Check out their video to discover why Nashik is the perfect place to work and live and start your journey with Decimal Point Analytics today! #CareerOpportunities #DecimalPointAnalytics #CareerGoals #NashikLiving #DecimalPointAnalytics

You cannot become a data analyst if you can’t do these things (shared the tools I use in the end)🔥🔥 Follow @onestopdata for data related content! ✅The most imp thing data analysts do is to understand the business requirements. (1) Gathering Data This means collecting data from different sources. Many a times this is done in collaboration with data engineers and architects hence usually the data analyst doesn’t have to do a lot in this. (2) Cleaning Data Going through the data and trying to understand it, making corrections where needed such as removing outliers or data that should not be included in the analysis. This step can take a lot of time, but understanding the data is crucial before you start to process it. (3) Processing data The data processing part of the process is where I use my skills and tools to analyze the work and come up with solutions for the problem at hand. (4) Creating reports for business leaders As an analyst, a lot of my time goes into creating and maintaining reports/dashboards for stakeholders and business leaders. This means showing the metrics and KPIs in the best manner possible to help drive business decisions. The best analysts are those that can use data to tell a story. (5) Collaborating with people This one is my favorite! As a data analyst, you work with many people across departments, both senior and junior. You’ll also likely collaborate closely with other people who work in data science like data architects and database developers. Tools I use: Excel,PowerBI,SQL and Python(sometimes) #dataanalytics #onestopdata #datacleaning #dataprocessing #dashboard #reports #sql #powerbi #excel #python

Day 1 - Inferential and Descriptive Statistics Hope this helpful, let me know if you want more such videos. Follow for more #datascience #machinelearning #womeninstem #learnintogether #progresseveryday #tech #consistency #statistics

Today’s focus: 👉 Filtering + Sorting + Logical Conditions + Pattern Matching These are used daily by Data Analysts. 1️⃣ WHERE Clause (Advanced Filtering) 2️⃣ LIKE Operator (Very Important 🔥) 3️⃣ ORDER BY (Sorting Data) 4️⃣ LIMIT / TOP (Depending on Database) 📘 Free Courses (with Certificates) 1. Great Learning Academy – Data Analytics using Excel 2. Great Learning Academy – Analytics with SQL & Python (Free) 3. Simplilearn SkillUp – Free Data Analyst Course Follow for more details ✨️ #datavisualization #dataanalytics #CorporateReels #capgeminiinindia #mumbai

Statistics made SIMPLE 🧠📊 These 10 concepts = 90% clarity Don’t just read… understand it. Save & revise later ✔️#StatisticsMadeEasy #CommerceWithVedant #StudySmart #Class12Commerce #CBSEStudents

1. Binomial Distribution: Perfect for yes/no situations. Like, what’s the chance of serving 99% correct meals in a restaurant tonight? 2. Geometric Distribution: Helps predict how many tries until success. Imagine a salesperson wondering when they’ll make their next sale. 3. Negative Binomial: Useful for multiple successes. A car dealership could use this to estimate their chances of selling 12 cars over a weekend. 4. Hypergeometric: Great for situations without replacement. Think of drawing colored balls from a bowl - what are the odds? 5. Poisson Distribution: Ideal for events over time or space. Banks use this to predict customer influx during peak hours. 6. Exponential Distribution: Perfect for estimating lifespans or waiting times. Businesses use this to predict when equipment might fail. #ProbabilityTheory #StatisticalDistributions #MathematicalModeling #DataScience #TheoreticalStatistics #StochasticProcesses #QuantitativeAnalysis #ProbabilityDistributions #MathematicalFoundations #AdvancedStatistics #machinelearning #dataanalytics #statistics

1. QUALIFY + ROW_NUMBER() Lets you rank rows and filter results in the same query — perfect for grabbing the most recent or top record without subqueries. 2. LAG / LEAD Used to look at the previous or next row — great for comparing changes over time (day-over-day, month-over-month). 3. CTE (WITH clause) Creates a temporary, named query so you can break complex SQL into clean, readable steps. #data #analyst #dayinthelife #dadlife #sql

Statistical methods mbs 1st semester Marks distribution

Learning data science, machine learning and AI isn't always about math @brilliantorg But if you want to be recognized as a true professional and build something significant, you should learn it. Brilliant allows you to learn the key math skills 1. Probability theory 2. Statistics 3. Linear algebra 4. Differential calculus On Brilliant you can also work on other skills like AI for beginners, data analysis and visualization and even machine learning and deep learning. Comment Brilliant to get started or check the info in my bio!

STRUCTURES FOR PIE CHARTS (Use these when describing proportions and percentages) 1. It is clearly evident from the pie charts above that... 2. The pie chart depicts (that)... 3. The graph, presented in the form of a pie chart, shows the general trend in... 4. The pie chart illustrates how... 5. The percentage remained steady at... 6. The figures in the pie chart are based on a survey of... STRUCTURES FOR BAR CHARTS (Use these to open your report clearly and accurately) 1. The bar chart illustrates that... 2. This is a column chart showing... 3. The bar chart shows / reveals / demonstrates / depicts / illustrates / presents trends in... 4. From the information given in the bar chart, it can be seen that... 5. The two bar charts above reflect... 6. The variations in the bar chart can be attributed to... #ielts #ieltsacademic #ielts_tips #ieltsexam #ieltsexamination

Statistics is overwhelming. There are a million concepts to learn. But for your first Data Science job, you don’t need to know EVERYTHING. I recommend focusing on this first list, AND pick 1-2 topics in the second list if you want to stand out. ✅ Statistics concepts you MUST KNOW to land your first Data Science job 1. Non-parametric tests – Kruskal-Wallis, Mann-Whitney 2. Bayesian hierarchical modeling 3. Time-series forecasting models (ARIMA, Holt-Winters) 4. Survival analysis, like Kaplan-Meier, Cox models 5. Advanced regression – LASSO, Ridge, ElasticNet 6. Mixed effects models 7. Deep learning – neural networks, CNNs, RNNs 8. Computer vision – image classification, object detection, segmentation 9. Natural language processing – embeddings, transformers, LLMs 10. Reinforcement learning – Q-learning, policy gradients 11. Advanced ML pipelines – feature stores, model registries, deployment ❌ Don’t worry about these concepts for now 1. Non-parametric tests – Kruskal-Wallis, Mann-Whitney 2. Bayesian hierarchical modeling 3. Time-series forecasting models (ARIMA, Holt-Winters) 4. Survival analysis, like Kaplan-Meier, Cox models 5. Advanced regression – LASSO, Ridge, ElasticNet 6. Mixed effects models 7. Deep learning – neural networks, CNNs, RNNs 8. Computer vision – image classification, object detection, segmentation 9. Natural language processing – embeddings, transformers, LLMs 10. Reinforcement learning – Q-learning, policy gradients 11. Advanced ML pipelines – feature stores, model registries, deployment #datascience #datascientist #machinelearning #aiengineering #statistics