Canva hiring Staff Data Scientist
https://www.lifeatcanva.com/en/jobs/6000000000324387/staff-data-scientist-marketing-research-data-remote-across-anz/
๐WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
๐Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best ๐๐
https://www.lifeatcanva.com/en/jobs/6000000000324387/staff-data-scientist-marketing-research-data-remote-across-anz/
๐WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
๐Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best ๐๐
๐1
Forwarded from Data Analyst Jobs
Meesho is hiring Data Scientist ๐
Experience : 1-2 Year
Location : Bangalore
Apply link : https://meesho.io/jobs/data-scientist--i?id=81b0947f-5a1e-4a51-93d5-bd63d954cf75
๐WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
๐Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best ๐๐
Experience : 1-2 Year
Location : Bangalore
Apply link : https://meesho.io/jobs/data-scientist--i?id=81b0947f-5a1e-4a51-93d5-bd63d954cf75
๐WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
๐Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best ๐๐
We are looking to hire AI/ML Engineers.
AI/ML Engineer
Full-time position with Vivid Edge Corp
Bangalore - Onsite (Hybrid)
If interested please share profile on my e-mail id : vijay.anand@vivid-edge.com
AI/ML Engineer
Full-time position with Vivid Edge Corp
Bangalore - Onsite (Hybrid)
If interested please share profile on my e-mail id : vijay.anand@vivid-edge.com
๐1
Planet is hiring Machine Learning Engineer (Remote)
https://job-boards.greenhouse.io/planetlabs/jobs/6670759
https://job-boards.greenhouse.io/planetlabs/jobs/6670759
job-boards.greenhouse.io
Planet
EY is hiring!
Position: Associate - Power BI/ SQL
Qualifications: Bachelor's degree
Salary: 4 - 10 LPA (Expected)
Experience: Freshers/ Experienced
Location: PAN India
๐Apply Now: https://careers.ey.com/ey/job/Kochi-Associate-BI-KL-682303/1187886701/
Position: Associate - Power BI/ SQL
Qualifications: Bachelor's degree
Salary: 4 - 10 LPA (Expected)
Experience: Freshers/ Experienced
Location: PAN India
๐Apply Now: https://careers.ey.com/ey/job/Kochi-Associate-BI-KL-682303/1187886701/
Tech Talent Hub hiring Data Scientist
Location : Dubai, United Arab Emirates
https://www.careers-page.com/tech-talent-hub-3/job/L79WWR8X?utm_medium=free_job_board&utm_source=linkedin
๐WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
๐Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best ๐๐
Location : Dubai, United Arab Emirates
https://www.careers-page.com/tech-talent-hub-3/job/L79WWR8X?utm_medium=free_job_board&utm_source=linkedin
๐WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
๐Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
All the best ๐๐
Forwarded from AI Jobs | Artificial Intelligence
Divami - Design & AI-Led Product Engineering is hiring Software Engineer
For 2021, 2022 gards
Location: Hyderabad
https://www.linkedin.com/jobs/view/4200591954
For 2021, 2022 gards
Location: Hyderabad
https://www.linkedin.com/jobs/view/4200591954
Linkedin
Divami - Design & AI-Led Product Engineering hiring Software Engineer (React) in Hyderabad, Telangana, India | LinkedIn
Posted 6:32:57 AM. The ideal candidate will be responsible for developing high-quality applications. They will also beโฆSee this and similar jobs on LinkedIn.
โค1
If you're into deep learning, then you know that students usually take one of the two paths:
- Computer vision
- Natural language processing (NLP)
If you're into NLP, here are 5 fundamental concepts you should know:
๐๐
https://t.me/generativeai_gpt/7
- Computer vision
- Natural language processing (NLP)
If you're into NLP, here are 5 fundamental concepts you should know:
๐๐
https://t.me/generativeai_gpt/7
๐1
Data Scientist Roadmap
|
|-- 1. Basic Foundations
| |-- a. Mathematics
| | |-- i. Linear Algebra
| | |-- ii. Calculus
| | |-- iii. Probability
| |
| | |
| |
| |
|
|
|-- 2. Data Exploration and Preprocessing
| |-- a. Exploratory Data Analysis (EDA)
| |-- b. Feature Engineering
| |-- c. Data Cleaning
| |-- d. Handling Missing Data
|
| | |
| |
| |
| |-- b. Unsupervised Learning
| | |-- i. Clustering
| | | |-- 1. K-means
| | | |-- 2. DBSCAN
| | |
| | |-- 1. Principal Component Analysis (PCA)
| | |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
| |
| |
|
|
|-- 4. Deep Learning
| |-- a. Neural Networks
| | |-- i. Perceptron
| |
| |
| |-- c. Recurrent Neural Networks (RNNs)
| | |-- i. Sequence-to-Sequence Models
| | |-- ii. Text Classification
| |
| |
|
|
|-- 5. Big Data Technologies
| |-- a. Hadoop
| | |-- i. HDFS
| |
| |
|
|
|-- 6. Data Visualization and Reporting
| |-- a. Dashboarding Tools
| | |-- i. Tableau
| | |-- ii. Power BI
| | |-- iii. Dash (Python)
| |
|
|-- 7. Domain Knowledge and Soft Skills
| |-- a. Industry-specific Knowledge
| |-- b. Problem-solving
| |-- c. Communication Skills
| |-- d. Time Management
|
|-- a. Online Courses
|-- b. Books and Research Papers
|-- c. Blogs and Podcasts
|-- d. Conferences and Workshops
`-- e. Networking and Community Engagement
|
|-- 1. Basic Foundations
| |-- a. Mathematics
| | |-- i. Linear Algebra
| | |-- ii. Calculus
| | |-- iii. Probability
| |
-- iv. Statistics
| |
| |-- b. Programming
| | |-- i. Python
| | | |-- 1. Syntax and Basic Concepts
| | | |-- 2. Data Structures
| | | |-- 3. Control Structures
| | | |-- 4. Functions
| | | -- 5. Object-Oriented Programming| | |
| |
-- ii. R (optional, based on preference)
| |
| |-- c. Data Manipulation
| | |-- i. Numpy (Python)
| | |-- ii. Pandas (Python)
| | -- iii. Dplyr (R)| |
|
-- d. Data Visualization
| |-- i. Matplotlib (Python)
| |-- ii. Seaborn (Python)
| -- iii. ggplot2 (R)|
|-- 2. Data Exploration and Preprocessing
| |-- a. Exploratory Data Analysis (EDA)
| |-- b. Feature Engineering
| |-- c. Data Cleaning
| |-- d. Handling Missing Data
|
-- e. Data Scaling and Normalization
|
|-- 3. Machine Learning
| |-- a. Supervised Learning
| | |-- i. Regression
| | | |-- 1. Linear Regression
| | | -- 2. Polynomial Regression| | |
| |
-- ii. Classification
| | |-- 1. Logistic Regression
| | |-- 2. k-Nearest Neighbors
| | |-- 3. Support Vector Machines
| | |-- 4. Decision Trees
| | -- 5. Random Forest| |
| |-- b. Unsupervised Learning
| | |-- i. Clustering
| | | |-- 1. K-means
| | | |-- 2. DBSCAN
| | |
-- 3. Hierarchical Clustering
| | |
| | -- ii. Dimensionality Reduction| | |-- 1. Principal Component Analysis (PCA)
| | |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
| |
-- 3. Linear Discriminant Analysis (LDA)
| |
| |-- c. Reinforcement Learning
| |-- d. Model Evaluation and Validation
| | |-- i. Cross-validation
| | |-- ii. Hyperparameter Tuning
| | -- iii. Model Selection| |
|
-- e. ML Libraries and Frameworks
| |-- i. Scikit-learn (Python)
| |-- ii. TensorFlow (Python)
| |-- iii. Keras (Python)
| -- iv. PyTorch (Python)|
|-- 4. Deep Learning
| |-- a. Neural Networks
| | |-- i. Perceptron
| |
-- ii. Multi-Layer Perceptron
| |
| |-- b. Convolutional Neural Networks (CNNs)
| | |-- i. Image Classification
| | |-- ii. Object Detection
| | -- iii. Image Segmentation| |
| |-- c. Recurrent Neural Networks (RNNs)
| | |-- i. Sequence-to-Sequence Models
| | |-- ii. Text Classification
| |
-- iii. Sentiment Analysis
| |
| |-- d. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
| | |-- i. Time Series Forecasting
| | -- ii. Language Modeling| |
|
-- e. Generative Adversarial Networks (GANs)
| |-- i. Image Synthesis
| |-- ii. Style Transfer
| -- iii. Data Augmentation|
|-- 5. Big Data Technologies
| |-- a. Hadoop
| | |-- i. HDFS
| |
-- ii. MapReduce
| |
| |-- b. Spark
| | |-- i. RDDs
| | |-- ii. DataFrames
| | -- iii. MLlib| |
|
-- c. NoSQL Databases
| |-- i. MongoDB
| |-- ii. Cassandra
| |-- iii. HBase
| -- iv. Couchbase|
|-- 6. Data Visualization and Reporting
| |-- a. Dashboarding Tools
| | |-- i. Tableau
| | |-- ii. Power BI
| | |-- iii. Dash (Python)
| |
-- iv. Shiny (R)
| |
| |-- b. Storytelling with Data
| -- c. Effective Communication|
|-- 7. Domain Knowledge and Soft Skills
| |-- a. Industry-specific Knowledge
| |-- b. Problem-solving
| |-- c. Communication Skills
| |-- d. Time Management
|
-- e. Teamwork
|
-- 8. Staying Updated and Continuous Learning|-- a. Online Courses
|-- b. Books and Research Papers
|-- c. Blogs and Podcasts
|-- d. Conferences and Workshops
`-- e. Networking and Community Engagement
โค1๐1
Forwarded from Python for Data Analysts
๐ฐ ๐๐ฅ๐๐ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐๐
These free, Microsoft-backed courses are a game-changer!
With these resources, youโll gain the skills and confidence needed to shine in the data analytics worldโall without spending a penny.
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/4jpmI0I
Enroll For FREE & Get Certified๐
These free, Microsoft-backed courses are a game-changer!
With these resources, youโll gain the skills and confidence needed to shine in the data analytics worldโall without spending a penny.
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/4jpmI0I
Enroll For FREE & Get Certified๐
๐1
I often get asked- what's the BEST Certification for #datascience or #machinelearning?
๐My answer is: none
The reality is that certification don't matter for data science.
This is not commerce. we are not using the same techniques over and over again to solve well-defined problems.
The problems are challenging, the data is messy and numerous techniques are used.
So if you've wondering which certification you should get, Save yourself,some mental energy and stop thinking about it- they are not really matter.
๐ Instead, grab a dataset and start playing with it.
๐ Start applying what you know and trying to solve interesting problems, learn something new every day.
๐ Here are few places to grab datasets to get you started
Google: https://toolbox.google.com/datasetsearch
Kaggle: https://www.kaggle.com/datasets
US Government Dataset: www.data.gov
Quandl: https://www.quandl.com/
UCI
ML repo: http://mlr.cs.umass.edu/ml/datasets.html
World Bank๐ฆ: https://data.worldbank.org/
๐My answer is: none
The reality is that certification don't matter for data science.
This is not commerce. we are not using the same techniques over and over again to solve well-defined problems.
The problems are challenging, the data is messy and numerous techniques are used.
So if you've wondering which certification you should get, Save yourself,some mental energy and stop thinking about it- they are not really matter.
๐ Instead, grab a dataset and start playing with it.
๐ Start applying what you know and trying to solve interesting problems, learn something new every day.
๐ Here are few places to grab datasets to get you started
Google: https://toolbox.google.com/datasetsearch
Kaggle: https://www.kaggle.com/datasets
US Government Dataset: www.data.gov
Quandl: https://www.quandl.com/
UCI
ML repo: http://mlr.cs.umass.edu/ml/datasets.html
World Bank๐ฆ: https://data.worldbank.org/
๐2
Forwarded from Python for Data Analysts
๐๐ฒ๐ฎ๐ฟ๐ป ๐ฃ๐ผ๐๐ฒ๐ฟ ๐๐ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ & ๐๐น๐ฒ๐๐ฎ๐๐ฒ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐๐ต๐ฏ๐ผ๐ฎ๐ฟ๐ฑ ๐๐ฎ๐บ๐ฒ!๐
Want to turn raw data into stunning visual stories?๐
Here are 6 FREE Power BI courses thatโll take you from beginner to proโwithout spending a single rupee๐ฐ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4cwsGL2
Enjoy Learning โ ๏ธ
Want to turn raw data into stunning visual stories?๐
Here are 6 FREE Power BI courses thatโll take you from beginner to proโwithout spending a single rupee๐ฐ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4cwsGL2
Enjoy Learning โ ๏ธ
Forwarded from AI Jobs | Artificial Intelligence
Airtel Machine Learning Engineer
https://in.linkedin.com/jobs/view/machine-learning-engineer-at-airtel-4193434706
https://in.linkedin.com/jobs/view/machine-learning-engineer-at-airtel-4193434706
Linkedin
airtel hiring Machine Learning Engineer in Gurugram, Haryana, India | LinkedIn
Posted 9:33:15 AM. Position: Lead โ Networks Analytics Job Description: This is a very inspiring role in NetworksโฆSee this and similar jobs on LinkedIn.
๐ Harvard study shows AI has effectively become equal to having a second human teammate
Two key points from the paper:
- In an experiment with 776 professionals at Procter & Gamble, individuals using AI performed about the same as teams without AI
- Teams using AI performed much better, often creating the best solutions. they also worked 12โ16% faster and gave longer, more detailed answers than those without AI
This indicates that AI has begun to match or replace human collaboration
Two key points from the paper:
- In an experiment with 776 professionals at Procter & Gamble, individuals using AI performed about the same as teams without AI
- Teams using AI performed much better, often creating the best solutions. they also worked 12โ16% faster and gave longer, more detailed answers than those without AI
This indicates that AI has begun to match or replace human collaboration
๐1
โ๏ธCGI Off Campus Drive 2025 Hiring Freshers For Associate Software Engineer Role | 4-8 LPAโ
๐จโ๐ปDesignation : Associate Software Engineer
๐Eligibility : BE/BTech
๐Batch : 2023 / 2024
๐ฐSalary : INR 4-8 LPA
โญ๏ธ Apply Fast : https://cgi.njoyn.com/corp/xweb/xweb.asp?CLID=21001&page=jobdetails&JobID=J0125-1910&lang=1
๐จโ๐ปDesignation : Associate Software Engineer
๐Eligibility : BE/BTech
๐Batch : 2023 / 2024
๐ฐSalary : INR 4-8 LPA
โญ๏ธ Apply Fast : https://cgi.njoyn.com/corp/xweb/xweb.asp?CLID=21001&page=jobdetails&JobID=J0125-1910&lang=1