Certification in Data Science

Data Science is a multidisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines skills from computer science, statistics, and domain expertise to analyze data and make data-driven decisions.

Key components of Data Science include:

• Data Collection: Gathering raw data from various sources.
  
  

• Data Cleaning: Preparing and preprocessing data to remove noise and errors.
  
  

• Data Analysis: Using statistical methods and tools to find patterns or trends.
  
  

• Machine Learning: Building predictive models using algorithms.
  
  

• Data Visualization: Presenting insights through graphs, charts, and dashboards.
  
  

• Decision Making: Using insights to guide business or research strategies.
  
  

Data Science is widely used in industries like healthcare, finance, marketing, and technology to improve operations, customer experience, and innovation.

Job after Data Science Course

Data Scientist – Analyze & interpret data.

Data Analyst – Generate insights from data.

Machine Learning Engineer – Build predictive models.

Business Intelligence Analyst – Support decisions with data.

Data Engineer – Manage data pipelines and storage.

 Syllabus for a Data Science course

Module 1: Introduction to Data Science

• What is Data Science?
  
  

• History and Applications
  
  

• Data Science Workflow
  
  

• Roles: Data Scientist, Analyst, Engineer
  
  

• Tools & Technologies in Data Science
  
  

Module 2: Mathematics and Statistics for Data Science

• Linear Algebra basics
  
  

• Probability theory
  
  

• Descriptive statistics (mean, median, mode, variance)
  
  

• Inferential statistics (hypothesis testing, p-value)
  
  

• Distributions (Normal, Binomial, Poisson)
  
  

• Correlation and regression
  
  

Module 3: Programming for Data Science (Python/R)

• Python basics: variables, loops, functions, OOP
  
  

• Libraries: NumPy, Pandas, Matplotlib, Seaborn
  
  

• Data structures & file handling
  
  

• Introduction to R (optional)
  
  

Module 4: Data Preprocessing and Cleaning

• Handling missing values
  
  

• Data imputation
  
  

• Outlier detection and treatment
  
  

• Encoding categorical variables
  
  

• Feature scaling (Normalization, Standardization)
  
  

Module 5: Data Visualization

• Introduction to data visualization
  
  

• Using Matplotlib, Seaborn, Plotly
  
  

• Creating dashboards with Power BI/Tableau
  
  

• Visualizing distributions, time series, and correlations
  
  

Module 6: Exploratory Data Analysis (EDA)

• Understanding dataset structure
  
  

• Summary statistics
  
  

• Correlation analysis
  
  

• Using visual and statistical techniques to explore data
  
  

Module 7: Machine Learning - Supervised Learning

• Introduction to ML
  
  

• Regression: Linear, Polynomial
  
  

• Classification: Logistic Regression, Decision Trees, Random Forest, SVM, KNN
  
  

• Model evaluation: confusion matrix, accuracy, precision, recall, F1-score
  
  

Module 8: Machine Learning - Unsupervised Learning

• Clustering: K-Means, Hierarchical, DBSCAN
  
  

• Dimensionality reduction: PCA, t-SNE
  
  

• Association Rule Mining (Apriori)
  
  

Module 9: Deep Learning and Neural Networks

• Introduction to Neural Networks
  
  

• Activation functions
  
  

• Forward and backpropagation
  
  

• CNNs and RNNs (basics)
  
  

• Tools: TensorFlow, Keras
  
  

Module 10: Natural Language Processing (NLP)

• Text preprocessing (Tokenization, Stopwords, Lemmatization)
  
  

• Bag of Words, TF-IDF
  
  

• Sentiment analysis
  
  

• Word embeddings (Word2Vec, GloVe)
  
  

• Text classification
  
  

Module 11: Big Data and Cloud Computing

• Introduction to Big Data (Hadoop, Spark)
  
  

• Cloud platforms: AWS, GCP, Azure (basics)
  
  

• Data storage and retrieval
  
  

• Working with large-scale data
  
  

Module 12: Capstone Project & Case Studies

• Real-world datasets
  
  

• End-to-end project implementation
  
  

• Documentation and presentation
  
  

• Domain-specific case studies (Healthcare, Finance, Retail, etc.)
  
  

Duration of Course  : 6 months

Fee : 30000/-

Contact for Admission

Siddharth  Sharma

HOD, Department of Computer Engineering

Concept IT Solutions, Pune

Call:7219116540