𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

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𝗚𝗼𝗼𝗴𝗹𝗲 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍 

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𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲 𝗣𝗿𝗲𝘃𝗶𝗲𝘄 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

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Data Scientist Roadmap
|
|-- 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

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𝗟𝗲𝗮𝗿𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 & 𝗘𝗹𝗲𝘃𝗮𝘁𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗚𝗮𝗺𝗲!😍

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𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗢𝗻 𝗗𝗲𝘃𝗼𝗽𝘀😍

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Interview questions asked by top product-based companies.

A friend of mine recently shared their interview journey, and I'd like to pass on what I learned about the data structures and algorithms (DSA) rounds.

👨🏾‍💻 Data Structures: He encountered questions on topics like arrays, strings, matrices, stacks, queues, and different types of linked lists (singly, doubly, and circular).

▶️ Algorithms: He was also interviewed on a wide array of algorithms like linear search, binary search, and sorting algorithms (bubble, quick, merge).

And faced questions on more challenging subjects like Greedy algorithms, Dynamic programming, and Graph algorithms.

🖛 Specifics: The devil lies in the details! His interview also delved into advanced topics such as Advanced Data Structures, Pattern Searching, Recursion, Backtracking, and Divide and Conquer strategies.

However, your ability to apply these concepts to real-world situations will undoubtedly set you apart from others.

On top, If you’re stuck at any of the above questions and need the right guidance in cracking top product-based company interviews,

As a community of tech enthusiasts, let's share our own interview experiences in the comments below. Together, we can learn from each other's experiences.

𝗔𝗰𝗰𝗲𝗻𝘁𝘂𝗿𝗲 𝗚𝗲𝗻𝗔𝗜 𝗛𝗮𝗰𝗸𝗮𝘁𝗵𝗼𝗻 𝟮𝟬𝟮𝟱 😍

Hack the Future: Join the Data and AI Revolution

In collaboration with Accenture and with GeeksforGeeks as the Community Partner, this event offers a unique opportunity to collaborate, learn, and innovate.

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With exciting cash prizes and networking opportunities, it's the perfect platform to join the Data and AI revolution.

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