• What is Data Science?
  – Understanding the sector and its importance
   History of Data Science
  – Evolution and milestones in Data Science
•  Data Science Process and Lifecycle
  – Steps concerned from statistics series to deployment
•  Data Science Roles
  – Data Scientist: Focus on analysis and modelling
  – Data Engineer: Handle facts infrastructure and pipelines
  -Data Analyst: Interpret and visualize statistics
•  Data Science Applications
  – How one-of-a-kind industries make use of facts technology (e.g., healthcare, finance, marketing)

•  Python Programming
  – Basics: Syntax, variables, and records kinds
  – Control Structures: Loops and conditional statements
  – Functions and Modules: Writing reusable code
  – Data Structures: Lists, tuples, dictionaries, units
  – Libraries: Using NumPy, Pandas for facts manipulation, Matplotlib, and Seaborn for visualization
• R Programming
  – Basics: Syntax, variables, information types
  – dplyr: Data manipulation
  – ggplot2: Data visualization
  -R for Statistical Analysis

• Data Manipulation
  – Importing and exporting data
  – Cleaning information: Handling missing values and outliers
  – Data transformation: Sorting, filtering, aggregation
  – Merging and joining datasets
•  Data Visualization
  – Basic plots: Line, bar, histogram, pie
  – Advanced visualizations: Boxplot, heatmap, pair plot
  – Interactive visualizations with Plotly
  – Dashboard advent with Tableau or Power BI

• Descriptive Statistics
  – Central tendency: Mean, median, mode
  – Dispersion: Range, variance, popular deviation
  – Skewness and kurtosis
• Inferential Statistics
  – Probability theory and distributions
  – Sampling and sampling distributions
  – Hypothesis testing: t-tests, chi-square check, ANOVA
  – Confidence durations and p-values
  – Correlation and causation

• Supervised Learning
  – Regression analysis: Linear, logistic
  – Classification algorithms: Decision timber, k-nearest acquaintances, aid vector machines, Naïve Bayes
  – Model evaluation: Confusion matrix, ROC curve, precision, recall, F1 score
• Unsupervised Learning
  – Clustering: K-means, agglomerative clustering, DBSCAN
  – Dimensionality discount: PCA, t-SNE
  Model Optimization
  – Cross-validation strategies
  – Hyperparameter tuning: Grid search, random seek
  – Ensemble techniques: Bagging, boosting, random wooded area, gradient boosting machines (GBM), XGBoost

•  Deep Learning
  – Introduction to neural networks
  – Frameworks: Tensor Flow, Kera’s, PyTorch
  – Convolutional Neural Networks (CNNs) for photograph processing
  – Recurrent Neural Networks (RNNs) for time series and NLP
  – Transfer gaining knowledge of and pre-trained models
• Deep Learning Techniques
  – Text preprocessing: Tokenization, lemmatization, forestall word removal
  – Sentiment analysis, subject matter modelling
  – Word embeddings: Word2Vec, GloVe
  – Sequence models: LSTM, GRU
• Time Series Analysis
  – Decomposition of time collection
  – ARIMA and exponential smoothing
  – Forecasting and anomaly detection

• Hadoop Ecosystem
  – Introduction to Hadoop and HDFS
  – The MapReduce programming version
  – Data processing with Apache Pig and Hive
• Apache Spark
  – Spark Core and RDDs
  – Spark SQL and Data Frames
  – Spark Streaming
  – Machine gaining knowledge of with MLlib
• NoSQL Databases
  – Introduction to NoSQL
  – Databases like MongoDB and Cassandra
  – Key-cost stores and document databases

The ETL Process
– Data ingestion: Batch and actual-time
– Data cleaning and transformation
– Data loading strategies

•  Data Pipeline Design
  – Pipeline orchestration with the use of Apache Airflow
  – Data integration gear: Talend, Informatica
• Cloud Data Engineering
  – AWS Data Services: S3, Redshift, Glue
  – Google Cloud Data Services: Big Query, Dataflow, Dataproc
  – Azure: Data Lake, Synapse Analytics

•  Jupyter Notebooks
  – Setup and use of Jupyter Notebooks
  – Interactive records technology workflows
•  Version Control
  – Git and GitHub for collaborative paintings
  – Managing code repositories
• Containerization
  – Introduction to Docker
  – Creating and dealing with Docker packing containers
•  Cloud Platforms
  – Deploying facts and technological know-how initiatives on AWS, Azure, and Google Cloud

• Project Planning
  – Defining the hassle and scope
  – Setting objectives
•  Data Collection and Preprocessing
  – Collecting records from numerous sources
  – Cleaning and getting ready information
•  Model Building and Analysis
  – Applying gadget learning algorithms
  – Tuning and optimizing fashions
• Result Presentation
  – Evaluating findings
  – Comprehensive reporting
  – User instructions

•  Effective Communication
  – Conveying statistics insights to non-technical audiences
•  Building a Portfolio
  – Documenting initiatives
  – Creating an internet presence (GitHub, LinkedIn)

– Crafting an information technology resume
– Preparing for technical interviews
– General interview suggestions

– Engaging with the statistics science network
– Participating in workshops and seminars