Overview of Data Science Program

A data science course typically provides comprehensive training in the interdisciplinary field of data science, which involves the extraction of knowledge and insights from structured and unstructured data. These courses are designed to equip students with the necessary skills to analyze and interpret complex data sets, make data-driven decisions, and develop predictive models.

  1. Introduction to Data Science:

    • Overview of data science and its applications.
    • Understanding the data science workflow.
    • Introduction to key concepts such as data cleaning, exploration, and visualization.

  2. Statistics and Mathematics:

    • Basic statistical concepts and methods.
    • Probability theory.
    • Linear algebra and calculus, as they relate to data analysis and machine learning.

  3. Programming Languages:

    • Proficiency in programming languages commonly used in data science, such as Python or R.
    • Application of programming languages for data manipulation, analysis, and visualization.

  4. Data Wrangling and Cleaning:

    • Techniques for handling missing data.
    • Data cleaning and preprocessing methods.

  5. Exploratory Data Analysis (EDA):

    • Visualizing data to gain insights.
    • Understanding distributions, patterns, and relationships in data.

  6. Machine Learning:

    • Supervised and unsupervised learning techniques.
    • Model training, evaluation, and deployment.
    • Classification, regression, clustering, and dimensionality reduction.

  7. Big Data Technologies:

    • Introduction to handling large datasets using technologies like Apache Hadoop and Apache Spark.

  8. Data Visualization:

    • Effective communication of insights through charts, graphs, and dashboards.
    • Use of tools like Matplotlib, Seaborn, or Tableau.

  9. Advanced Topics:

    • Deep learning and neural networks.
    • Natural Language Processing (NLP) for text data analysis.
    • Time-series analysis and forecasting.

  10. Capstone Project:

    • Many data science courses include a hands-on project where students apply their skills to solve a real-world problem or analyze a large dataset.

  11. Ethical Considerations:

    • Discussions on ethical issues related to data science, including privacy concerns and bias in algorithms.

  12. Industry-Relevant Tools and Technologies:

    • Exposure to popular tools and platforms used in the industry, such as Jupyter Notebooks, GitHub, and cloud platforms like AWS or Azure.

Remember that the specific content and emphasis may vary between different data science courses. Additionally, some courses might focus more on specific aspects, such as machine learning, while others provide a broader overview of the entire data science ecosystem. It’s essential to choose a course that aligns with your learning goals and interests.

About Michael.

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Live Project

We provide the Real-time Projects execution platform with the best-learning Experience for the students with Project and chance to get hire.

Placement Support

We have protected tie-up with more than 600+ leading Small & Medium Companies to Support the students. once they complete the course.

Certifications

Globally recoganized certification on course completion, and get best exposure in handling live tools & management in your projects.

Affordable Fees

We serve the best for the students to implement their passion for learning with an affordable fee. You also have instalment to pay your fees.

Flexibility

We intend to provide a great learning atmosphere for the students with flexible modes like Classroom or Online Training with fastrack mode

Syllabus - Data Science

Module 1: Introduction to R (Duration: 2Hrs)
• What is R?
• Why R?
• Installing R
• R environment
• How to get help in R
• R Studio Overview
Module 2: R Basics (Duration: 5Hrs)
• Environment setup
• Data Types
• Variables
• Vectors
• Lists
• Matrix
• Array
• Factors
• Data Frames
• Loops
• Packages
• Functions
• In-Built Data sets
Module 3: R Packages (Duration: 3Hrs)
• DMwR
• Dplyr/plyr
• Caret
• Lubridate
• E1071
• Cluster/fpc
• table
• Stats/utils
• Ggplot/ggplot2
• Glmnet
Module 4: Machine Learning using R (Duration: 10Hrs)
• Linear Regression
• Logistic Regression
• K-Means
• K-Means++
• Hierarchical Clustering – Agglomerative
• CART
• c5.0
• Random forest
• Naïve Bayes
Data Science with Python
Module 1: Introduction to Data Science (Duration: 1Hr)
• What is Data Science?
• What is Machine Learning?
• What is Deep Learning?
• What is AI?
• Data Analytics & it’s types
Module 2: Introduction to Python (Duration: 1Hr)
• What is Python?
• Why Python?
• Installing Python
• Python IDEs
• Jupyter Notebook Overview
Module 3: Python Basics (Duration: 5Hrs)
• Python Basic Data types
• Lists
• Slicing
• IF statements
• Loops
• Dictionaries
• Tuples
• Functions
• Array
• Selection by position & Labels
Module 4: Python Packages (Duration: 2Hrs)
• Pandas
• Numpy
• Sci-kit Learn
• Mat-plot library
Module 5: Importing data (Duration: 1Hr)
• Reading CSV files
• Saving in Python data
• Loading Python data objects
• Writing data to csv file
Module 6: Manipulating Data (Duration: 1Hr)
• Selecting rows/observations
• Rounding Number
• Selecting columns/fields
• Merging data
• Data aggregation
• Data munging techniques
Module 7: Statistics Basics (Duration: 11Hrs)
• Central Tendency
• Mean
• Median
• Mode
• Skewness
• Normal Distribution
• Probability Basics
• What does mean by probability?
• Types of Probability
• ODDS Ratio?
• Standard Deviation
• Data deviation & distribution
• Variance
• Bias variance Trade off
• Underfitting
• Overfitting
• Distance metrics
• Euclidean Distance
• Manhattan Distance
• Outlier analysis
• What is an Outlier?
• Inter Quartile Range
• Box & whisker plot
• Upper Whisker
• Lower Whisker
• Scatter plot
• Cook’s Distance
• Missing Value treatments
• What is a NA?
• Central Imputation
• KNN imputation
• Dummification
• Correlation
• Pearson correlation
• Positive & Negative correlation
Module 8: Error Metrics (Duration: 3Hrs)
• Classification
• Confusion Matrix
• Precision
• Recall
• Specificity
• F1 Score
• Regression
• MSE
• RMSE
• MAPE
Machine Learning
Module 9: Supervised Learning (Duration: 6Hrs)
• Linear Regression
• Linear Equation
• Slope
• Intercept
• R square value
• Logistic regression
• ODDS ratio
• Probability of success
• Probability of failure
• ROC curve
• Bias Variance Tradeoff
Module 10: Unsupervised Learning (Duration: 4Hrs)
• K-Means
• K-Means ++
• Hierarchical Clustering
Module 11: Other Machine Learning algorithms (Duration: 10Hrs)
• K – Nearest Neighbour
• Naïve Bayes Classifier
• Decision Tree – CART
• Decision Tree – C50
• Random Forest
Tableau
Module 1: Tableau Course Material (Duration: 5Hrs)
• Start Page
• Show Me
• Connecting to Excel Files
• Connecting to Text Files
• Connect to Microsoft SQL Server
• Connecting to Microsoft Analysis Services
• Creating and Removing Hierarchies
• Bins
• Joining Tables
• Data Blending
Module 2: Learn Tableau Basic Reports (Duration: 5Hrs)
• Parameters
• Grouping Example 1
• Grouping Example 2
• Edit Groups
• Set
• Combined Sets
• Creating a First Report
• Data Labels
• Create Folders
• Sorting Data
• Add Totals, Sub Totals and Grand Totals to Report
Module 3: Learn Tableau Charts (Duration: 4Hrs)
• Area Chart
• Bar Chart
• Box Plot
• Bubble Chart
• Bump Chart
• Bullet Graph
• Circle Views
• Dual Combination Chart
• Dual Lines Chart
• Funnel Chart
• Traditional Funnel Charts
• Gantt Chart
• Grouped Bar or Side by Side Bars Chart
• Heatmap
• Highlight Table
• Histogram
• Cumulative Histogram
• Line Chart
• Lollipop Chart
• Pareto Chart
• Pie Chart
• Scatter Plot
• Stacked Bar Chart
• Text Label
• Tree Map
• Word Cloud
• Waterfall Chart
Module 4: Learn Tableau Advanced Reports (Duration: 6Hrs)
• Dual Axis Reports
• Blended Axis
• Individual Axis
• Add Reference Lines
• Reference Bands
• Reference Distributions
• Basic Maps
• Symbol Map
• Use Google Maps
• Mapbox Maps as a Background Map
• WMS Server Map as a Background Map
Module 5: Learn Tableau Calculations & Filters (Duration: 6Hrs)
• Calculated Fields
• Basic Approach to Calculate Rank
• Advanced Approach to Calculate Rank
• Calculating Running Total
• Filters Introduction
• Quick Filters
• Filters on Dimensions
• Conditional Filters
• Top and Bottom Filters
• Filters on Measures
• Context Filters
• Slicing Fliters
• Data Source Filters
• Extract Filters
Module 6: Learn Tableau Dashboards (Duration :4Hrs)
• Create a Dashboard
• Format Dashboard Layout
• Create a Device Preview of a Dashboard
• Create Filters on Dashboard
• Dashboard Objects
• Create a Story