1. Foundation in Mathematics and Statistics
Linear Algebra: Vectors, matrices, eigenvalues/eigenvectors.
Calculus: Derivatives, integrals, optimization.
Probability and Statistics: Probability distributions, hypothesis testing, descriptive statistics.
2. Programming Skills
Python: Libraries like NumPy, pandas, matplotlib, scikit-learn.
R: Data manipulation and statistical analysis.
SQL: Querying databases, joins, aggregations.
3. Data Manipulation and Cleaning
Data Wrangling: Handling missing values, data transformations, data normalization.
ETL (Extract, Transform, Load): Tools like Apache Airflow, Talend.
4. Exploratory Data Analysis (EDA)
Visualization Tools: Matplotlib, seaborn, ggplot2.
Descriptive Statistics: Summarizing data distributions and identifying patterns.
5. Machine Learning
Supervised Learning: Regression, classification (linear regression, logistic regression, decision trees, SVMs).
Unsupervised Learning: Clustering (K-means, hierarchical), dimensionality reduction (PCA, t-SNE).
Model Evaluation: Cross-validation, confusion matrix, ROC-AUC.
6. Deep Learning
Neural Networks: Basics of neurons, layers, activation functions.
Frameworks: TensorFlow, Keras, PyTorch.
Advanced Topics: CNNs for image data, RNNs for sequential data.
7. Big Data Technologies
Hadoop Ecosystem: HDFS, MapReduce, Hive.
Spark: Data processing at scale with PySpark.
NoSQL Databases: MongoDB, Cassandra.
8. Data Engineering
Data Pipelines: Building and managing data workflows.
Tools: Apache Kafka, Apache NiFi.
9. Cloud Computing
Platforms: AWS, Google Cloud Platform (GCP), Azure.
Services: AWS S3, EC2, Lambda; GCP BigQuery, Dataflow; Azure Data Lake.
10. Domain Knowledge
Understanding the specific industry you're working in (e.g., finance, healthcare, e-commerce).
Tailoring models and analysis to address domain-specific challenges.
11. Soft Skills
Communication: Presenting findings, storytelling with data.
Collaboration: Working with cross-functional teams.
Critical Thinking: Problem-solving and decision-making.