#lejhro

[deeones]

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[mvishnukumar]

How Augmented Analytics Transforms Business Intelligence Practices?

Hi,

Augmented analytics is changing the way businesses approach data analysis by integrating AI and machine learning into traditional business intelligence tools. Instead of relying on human analysts to sift through data manually, augmented analytics tools can automate much of the process. These tools can clean and organize data, find patterns, and even suggest actionable insights. This makes it easier for business users, even those without technical expertise, to get the information they need to make data-driven decisions. 

For example, sales teams can use augmented analytics to quickly identify which products are performing well and adjust their strategies accordingly. The result is faster, more efficient decision-making across the organization, with insights that are accessible to everyone, not just data scientists.

[gnh5blog]

A TURN FROM B.Com OR BBA GRADUATE TO 

DATA ANALYST

The business world is changing, and so are the opportunities within it. If you've finished your studies in Bachelor of Commerce (B.Com) or Bachelor of Business Administration (BBA), you might be wondering how to switch into the field of data analysis. Data analysts play an important role these days, finding useful information in data to help with decisions. In this blog post, we'll look at the steps you can take to smoothly change from a B.Com or BBA background to the exciting world of data analysis.

What You Already Know:

Even though it might feel like a big change, your studies in B.Com or BBA have given you useful skills. Your understanding of how businesses work, finances, and how organisations operate is a great base to start from.

Step 1: Building Strong Data Skills:

To make this change, you need to build a strong foundation in data skills. Begin by getting to know basic statistics, tools to show data visually, and programs to work with spreadsheets. These basic skills are like building blocks for learning about data.

I would like to suggest the best online platform where you can learn these skills. Lejhro bootcamp has courses that are easy to follow and won't cost too much.

Step 2: Learning Important Tools:

Data analysts use different tools to work with data. Learning how to use tools like Excel, SQL, and Python is really important. Excel is good for simple stuff, SQL helps you talk to databases, and Python is like a super tool that can do lots of things.

You can learn how to use these tools online too. Online bootcamp courses can help you get good at using them.

Step 3: Exploring Data Tricks:

Understanding how to work with data is the core of being a data analyst. Things like looking closely at data, testing ideas, figuring out relationships, and making models are all part of it. Don't worry, these sound fancy, but they're just different ways to use data.

Step 4: Making a Strong Collection:

A collection of things you've done, like projects, is called a portfolio. You can show this to others to prove what you can do. As you move from B.Com or BBA to data analysis, use your business knowledge to pick projects. For example, you could study how sales change, what customers do, or financial data.

Write down everything you do for these projects, like the problem, the steps you took, what tools you used, and what you found out. This collection will show others what you're capable of.

Step 5: Meeting People and Learning More:

Join online groups and communities where people talk about data analysis. This is a great way to meet other learners, professionals, and experts in the field. You can ask questions and talk about what you're learning.

LinkedIn is also a good place to meet people. Make a strong profile that shows your journey and what you can do. Follow data analysts and companies related to what you're interested in to stay up to date.

Step 6: Gaining Experience:

While you learn, it's also good to get some real experience. Look for internships, small jobs, or freelance work that lets you use your skills with real data. Even if the job isn't all about data, any experience with data is helpful.

Step 7: Updating Your Resume:

When you're ready to apply for data jobs, change your resume to show your journey. Talk about your B.Com or BBA studies, the skills you learned, the courses you took, your projects, and any experience you got. Explain how all of this makes you a great fit for a data job.

Using Lejhro Bootcamp:

When you're thinking about becoming a data analyst, think about using Lejhro Bootcamp. They have a special course just for people like you, who are switching from different fields. The Bootcamp teaches you practical things, has teachers who know what they're talking about, and helps you find a job later.

Moving from B.Com or BBA to a data analyst might seem big, but it's totally doable. With practice, learning, and real work, you can make the switch. Your knowledge about business mixed with data skills makes you a special candidate. So, get ready to learn, practice, and show the world what you can do in the world of data analysis!

***********

[blogbyahad]

What are some effective ways for beginners in data science to learn from other data scientists?

1. Online Courses and MOOCs

Platforms: Websites like Lejhro Bootcamp, edX, and Udacity offer courses taught by industry experts. Engaging with these can provide insights into real-world applications.

2. Mentorship

Finding a Mentor: Look for mentors in your network or through platforms like LinkedIn. Having a mentor can provide personalized guidance and feedback.

3. Community Engagement

Forums and Groups: Join online communities such as Reddit (r/datascience), Stack Overflow, or data science-focused Slack groups to ask questions and participate in discussions.

Meetups and Conferences: Attend local meetups, workshops, and conferences to network and learn from professionals in the field.

4. Collaborative Projects

Kaggle Competitions: Participate in Kaggle competitions where you can collaborate with others, learn from shared code, and see different approaches to problem-solving.

Open Source Projects: Contribute to open source data science projects on GitHub, where you can learn from the code written by others.

5. Blogging and Writing

Writing about Your Learning: Start a blog or contribute to platforms like Medium, sharing your learning journey. Engaging with feedback can deepen your understanding and connect you with others.

6. Reading Books and Research Papers

Books: Read foundational books on data science and specific topics (e.g., “Python for Data Analysis,” “Deep Learning”).

Papers: Follow recent research in data science through platforms like arXiv or Google Scholar to see how experts are tackling problems.

7. Webinars and Podcasts

Listening to Experts: Attend webinars and listen to podcasts featuring experienced data scientists discussing their work, challenges, and insights.

8. Social Media Engagement

Twitter and LinkedIn: Follow data scientists on social media platforms to stay updated on trends and discussions in the field.

9. Practice with Real-World Datasets

Exploratory Data Analysis: Work on publicly available datasets from sources like UCI Machine Learning Repository or Kaggle Datasets, applying techniques you’ve learned and comparing your approach to others.

10. Feedback and Code Reviews

Seek Feedback: Share your projects and analyses with peers or online communities for constructive criticism and suggestions.

[pandeypankaj]

How do I learn R, Python and data science?

Learning R, Python, and Data Science: A Comprehensive Guide

Choosing the Right Language

R vs. Python: Both R and Python are very powerful tools for doing data science. R is usually preferred for doing statistical analysis and data visualisations, whereas Python is much more general and currently is very popular for machine learning and general-purpose programming. Your choice of which language to learn should consider your specific goals and preferences.

Building a Strong Foundation

Structured Courses Online Courses and Tutorials: Coursera, edX, and Lejhro offer courses and tutorials in R and Python for data science. Look out for courses that develop theoretical knowledge with practical exercises. Practise your skills with hands-on coding challenges using accompanying datasets, offered on websites like Kaggle and DataCamp.

Books: There are enough books to learn R and Python for data science. You may go through the classical ones: "R for Data Science" by Hadley Wickham, and "Python for Data Analysis" by Wes McKinney.

Learning Data Science Concepts

Statistics: Know basic statistical concepts: probability, distribution, hypothesis testing, and regression analysis.

Cleaning and Preprocessing: Learn how to handle missing data techniques, outliers, and data normalisation.

Data Visualization: Expert libraries to provide informative visualisations, including but not limited to Matplotlib and Seaborn in Python and ggplot2 in R.

Machine Learning: Learn algorithms-Linear Regression, Logistic Regression, Decision Trees, Random Forest, Neural Networks, etc.

Deep Learning: Study deep neural network architecture and how to build and train them using the frameworks TensorFlow and PyTorch.

Practical Experience

Personal Projects: In this, you apply your knowledge to personal projects which help in building a portfolio.

Kaggle Competitions: Participate in Kaggle competitions to solve real-world problems in data science and learn from others. 

Contributions to Open-Source Projects: Contribute to some open-source projects for data science in order to gain experience and work with other people. 

Other Advice

Join Online Communities: Join online forums or communities such as Stack Overflow and Reddit to ask questions, get help, and connect with other data scientists.

Attend Conferences and Meetups: This is a fantastic way to network with similar working professionals in the field and know the latest trends going on in the industry.

Practice Regularly: For becoming proficient in data science, consistent practice is an indispensable element. Devote some time each day for practising coding challenges or personal projects.

This can be achieved by following the above-mentioned steps and having a little bit of dedication towards learning R, Python, and Data Science.

[md-sadique]

Struggling with a concept? No worries, the Data Science Bootcamp fam is here to help!

[hirelinkup]

Technical Support Engineer at LEJHRO | Remote job 2024

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[mvishnukumar]

What is the role of data preprocessing in data science, and why is it important?

Hi,

Data preprocessing is like the foundation of any good data science project. Raw data is often messy; it can have missing values, duplicates, outliers, or inconsistent formats, and using this directly in your machine learning model can lead to poor results. Preprocessing helps clean and prepare this data so your algorithms can work better. This process involves several steps. First, you might need to handle missing data by either filling it in with average values or removing incomplete entries altogether. Then comes data transformation, where you standardize or normalize the data so that everything is on the same scale. You might also remove outliers that could distort your model’s accuracy. Another key part of preprocessing is feature selection and engineering creating new features or selecting the most important ones. 

All of this ensures that your machine learning models are being fed high-quality data, which leads to more accurate and reliable predictions.

[pranoti-t]

In today's data-driven world, the scope of data science is boundless, and its potential is awe-inspiring! 🚀💻

From predicting consumer behavior to optimizing business operations, data science holds the key to unlocking valuable insights that drive innovation and success. As technology advances, so does the demand for skilled data scientists who can make sense of complex datasets and transform them into actionable solutions.

The future of data science is bright, with exciting prospects across industries. From healthcare and finance to marketing and cybersecurity, every sector is embracing the power of data to make informed decisions and gain a competitive edge.

In the coming years, artificial intelligence (AI) and machine learning (ML) will further amplify the impact of data science, revolutionizing the way we live and work. By leveraging algorithms and predictive models, data scientists will transform vast amounts of information into meaningful patterns, enabling personalized experiences, automation, and smarter decision-making.

If you're intrigued by the limitless possibilities of data science, join the journey today @lejhro! Equip yourself with the essential skills, immerse yourself in real-world projects, and embrace the ever-evolving field of data science. Register now: https://www.bootcamp.lejhro.com/data-science-course-training

https://www.bootcamp.lejhro.com/

The future is data-driven, and you can be at the forefront of this exciting revolution. Start your data science adventure now and be part of shaping a brighter tomorrow!

DataScience #FutureTech #Analytics #BigData #AI #MachineLearning #TechRevolution #CareerOpportunities #DataDriven #FutureProspects

[pandeypankaj]

What is the best way to learn Python quickly and effectively?

How to Learn Python Fast

1. Laying the Basics Right

Online Tutorials and Courses: Websites such as Coursera, edX, and Lejhro have structured courses in various teaching styles.

Interactive Coding Sites: Sites like Codecademy, HackerRank, and LeetCode offer hands-on practice-cum-immediate feedback.

2. Do Practice Regularly

Building Projects: As you go further, implement your knowledge by building minor projects. It helps you get used to the concept and visualizes the usage of Python in real-world applications.

Practice coding challenges: Sites such as Project Euler and Rosalind can help you pinpoint your weaknesses in coding, providing you with problems to improve your skills in problem solving.

3. Join the Community

Online forums and communities: Websites like Stack Overflow and a sub-Reddit named r/learnpython get you help for your questions or just simply hang out with other people learning.

Local meetups: Find Python meetups happening around your area and attend. This will give you contacts with advanced Python programmers where you'll be privileged to listen and learn from their experiences.

4. Learning from Others

Mentorship: Find yourself a mentor who will be able to help you out, answer some questions, and review your code.

Open-source projects: Contribute to open-source projects written in Python. You will be able to learn from senior developers and see how real projects look.

5. Effective Learning

Spaced repetition: Go through regular intervals at which you review for better reinforcement of memory.

Active recall: Test your knowledge by trying to recall something without looking at your notes or resources.

Pomodoro Technique: Break your study into relatively small, focused intervals separated by short breaks.

6. Motivate yourself

Setting reachable objectives: Break down your learning into smaller steps to help you stay motivated.

Celebrate your progress: Give yourself rewards after reaching milestones.

7. Try and Explore

Try different approaches: Do not hesitate to try different ways of learning until you find what works best for you.

Python Libraries: Delve into the world of Python's vast ecosystem of libraries and tools to aid in expanding your knowledge and skill.

Practice these tips habitually, and you will be able to learn Python well enough to attain proficiency in Data Science.

[bestdatasciencebootcamp]

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[md-sadique]

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