Roadmap to Data Science

Data science tasks in the order of complexity

1. Fundamental knowledge
   - Basic mathetics (Linear algebra, Calculus, Probability, and statistics)
   - Programming languages: Python;
   - Database management tools: SQL;
   - Interactive visualization tools: Power Bi, and Tableau;
   - MS-Excel
2. Data manipulation and visualization
   - Data manipulation Python libraries: Numpy, Pandas
   - Data visualization Python libraries: Matplotlib, Seaborn
   - Other libraries: Plotly, ggplot2(R-programming), Dash
3. Some Exploratory data analysis projects steps
   - Exploratory data analysis techniques
   - Data Cleaning
   - Handling missing data
   - Data Scaling and normalization
   - Outliers detection and treatment.
4. Machine learning
   - Supervised learning
   - Unsupervised learning
   - Reinforcement learning
   - Model evaluation and validation
5. Deep learning
   - neural network
   - Convolution neural networks (CNNs)
   - Recurrent Neural networks (RNNs)
   - long Short-term memory (LSTM) and Gated Recurrent Units (GRU)
   - Generative Adversaarial networks (GANs)
6. Some advanced topics
   - Natural Language Processing (NLP): Text processing, word Embeddings (Word2Vec, Glove), Recurrent Neural Networks for NLP
   - Time Series Analysis (Time-series Decomposition, Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA, Exponential Smoothing Methods, Prophet)
   - Bayesian Statistics and Probabilistic Programming (Bayesian Inference, Markov Chain MMonte Carlo (MCMC))
7. Big Data Technologies
   - Hadoop
   - MapReduce
   - HDFS
   - Spark (RDDs, DataFrames, Mlip)
   - NoSQL databases (MongoDB, Cassandra, HBase, Couchbase)
   - Stream Processing frameworks (Apache Kafka, AApache Flink, Apache Storm)

(Credit: Waleed Mousa)