Statistics and Probability:
Understanding basic statistical concepts like mean, median, mode, variance, standard deviation.
Probability theory including Bayes' theorem, probability distributions (normal, binomial, Poisson, etc.).
Hypothesis testing, confidence intervals, and p-values.
Machine Learning:
Supervised learning: Regression (linear regression, logistic regression), classification (decision trees, random forests, support vector machines), ensemble methods (bagging, boosting).
Unsupervised learning: Clustering (k-means, hierarchical clustering), dimensionality reduction (principal component analysis, t-SNE).
Evaluation metrics for machine learning models (accuracy, precision, recall, F1-score, ROC-AUC, etc.).
Model selection and hyperparameter tuning.
Data Manipulation and Cleaning:
Data preprocessing: Handling missing data, dealing with outliers, normalization, scaling.
Feature engineering: Creating new features, transforming variables, dealing with categorical data.
Data integration and merging datasets.
Data Visualization:
Plotting libraries like Matplotlib, Seaborn, Plotly (Python), ggplot2 (R).
Visualizing distributions, trends, relationships between variables.
Creating interactive visualizations and dashboards.
Big Data Technologies:
Hadoop ecosystem: HDFS, MapReduce, Hive, Pig.
Apache Spark: RDDs, DataFrames, Spark SQL, MLlib.
Deep Learning:
Neural network architecture: Perceptrons, feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs).
Deep learning frameworks: TensorFlow, Keras, PyTorch.
Applications in computer vision, natural language processing, and speech recognition.
Natural Language Processing (NLP):
Text preprocessing: Tokenization, stemming, lemmatization.
NLP tasks: Named entity recognition, sentiment analysis, text classification, language translation.
NLP libraries: NLTK, spaCy, Gensim.
Time Series Analysis:
Decomposition, smoothing techniques.
Forecasting methods: ARIMA, exponential smoothing, Prophet.
Anomaly detection in time series data.
Database Systems and SQL:
Relational database concepts.
SQL querying: SELECT, JOIN, GROUP BY, HAVING.
Working with databases using Python libraries like SQLAlchemy.
Optimization Techniques:
Gradient descent algorithms: Batch gradient descent, stochastic gradient descent.
Optimization for machine learning models: Regularization techniques (L1, L2), optimization algorithms (Adam, RMSprop).
Cloud Computing:
Cloud platforms: AWS, Azure, Google Cloud Platform.
Setting up cloud-based data pipelines, storage, and computing resources.
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