Hello there,

The atomic structure has evolved through various scientific discoveries:

1. Dalton’s Atomic Theory : John Dalton (1808) proposed that atoms are indivisible particles that make up elements. He suggested each element’s atoms are identical but differ from those of other elements.
   
   

2. Thomson’s Model : J.J. Thomson (1897) discovered the electron and proposed the "plum pudding" model, where negatively charged electrons were embedded in a positive sphere.
   
   

3. Rutherford’s Model : Ernest Rutherford (1911) discovered the nucleus by conducting the gold foil experiment. He proposed that an atom has a dense, positively charged nucleus with electrons orbiting around it.
   
   

4. Bohr’s Model : Niels Bohr (1913) refined Rutherford's model by introducing fixed orbits or energy levels where electrons revolve without radiating energy.
   
   

5. Quantum Mechanical Model : Developed by Schrödinger and Heisenberg, this modern model describes electrons as probability clouds, or orbitals, rather than fixed paths. It incorporates principles of quantum mechanics.

These contributions collectively advanced our understanding of atomic structure.

For more details you can check out an article by Careers360 whose link is given below:

Link: https://www.careers360.com/chemistry/structure-of-the-atom-chapter-pge

I hope this answer helps you. If you have more queries then feel free to share your questions with us we will be happy to assist you.

Thank you and wishing you all the best for your bright future.

Hello aspirant,

A candidate must major in one of the following four subjects during their undergraduate and graduate studies: biology, chemistry, physics, or mathematics. The candidates must show up for a number of entrance exams in order to be considered for this. Exams such as the NET and GATE must be taken by the candidate.

For more information, please visit the following link:

https://www.careers360.com/careers/scientist

Thank you

Python is the go-to language for data science. Proficiency in Python libraries like NumPy, Pandas, Matplotlib, and Scikit-learn is essential. If you're familiar with other languages like R or Java, that's a plus, but focus on mastering Python for data science.

Brush up on core statistical concepts like hypothesis testing, regression analysis, and probability. Learn the fundamentals of machine learning algorithms like linear regression, decision trees, random forests, and classification methods. Brush up on core statistical concepts like hypothesis testing, regression analysis, and probability. Learn the fundamentals of machine learning algorithms like linear regression, decision trees, random forests, and classification methods.

Learn how to acquire data from various sources, handle missing values, and clean and manipulate data for analysis.

Being able to query databases to retrieve relevant data is a valuable skill for data scientists.


https://www.careers360.com/download/ebooks/beginners-guide-data-science

I hope it helps!

Effective communication of insights is crucial. Master data visualization tools like Tableau, Power BI, or libraries like Seaborn and Matplotlib to create clear and impactful visualizations.

Given your background in CRM, you're already equipped with valuable skills that can be directly applied to the field of data science. Your understanding of customer data, analytics, and business processes will be a significant asset.

Essential Courses and Certifications:

1. Foundation in Data Science:

   • Python Programming: A solid foundation in Python is crucial for data science tasks.
   • Statistics: Understand statistical concepts like probability, distributions, hypothesis testing, and regression analysis.
   • Linear Algebra: Grasp matrix operations, vector spaces, and eigenvalues for data manipulation.

2. Data Analysis and Visualization:

   • Data Cleaning and Preprocessing: Learn techniques to handle missing values, outliers, and data normalization.
   • Data Visualization: Master libraries like Matplotlib, Seaborn, and Plotly to create informative visualizations.
   • Exploratory Data Analysis (EDA): Gain proficiency in exploring datasets to uncover patterns and insights.

3. Machine Learning:

   • Supervised Learning: Dive into algorithms like linear regression, logistic regression, decision trees, random forests, and support vector machines.
   • Unsupervised Learning: Explore clustering algorithms (k-means, hierarchical clustering) and dimensionality reduction techniques (PCA, t-SNE).
   • Deep Learning: Understand neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

4. Specialized Courses for Data Science in CRM:

   • Customer Segmentation: Learn techniques to group customers based on their characteristics and behaviors.
   • Customer Churn Prediction: Understand models to predict customer attrition and take preventive measures.
   • Customer Lifetime Value (CLTV) Analysis: Calculate and analyze the long-term value of customers.
   • Recommendation Systems: Explore collaborative filtering and content-based filtering algorithms.

Recommended Certifications:

• Certified Data Scientist (CDS) from SAS: This certification validates your data science skills and knowledge.
• Data Scientist Associate (DSA) from IBM: A comprehensive certification covering data science fundamentals.
• Certified Analytics Professional (CAP) from INFORMS: A globally recognized certification for analytics professionals.

Online Platforms:

• Coursera: Offers courses from top universities like Stanford, University of Michigan, and Google.
• edX: Provides courses from institutions like MIT, Harvard, and UC Berkeley.
• DataCamp: A platform focused on data science and data analysis.
• Kaggle: A community platform for data scientists to practice and compete in data challenges.