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High Level Project Management Overview Blog – Data Science

Fast-track your career with the Certification Programme in Data Science. Master all the key tools and techniques in Data Science and pick up domain-specific skills to add more value to your profile.

Data Science Programme Overview

The Data Scientist Certification Programme is one of the most comprehensive Data Scientist courses in Aurangabad. It is specially designed to suit both data professionals and beginners who want to make a career in this fast-growing profession. Over 3 months, students will learn key techniques such as Statistical Analysis, Regression Analysis, Data Mining, Machine Learning, Forecasting and Text Mining, and tools such as Python and R Programming.

Course Details

Modules

Business Problem

  • Business Objective – Minimize Defaulters / Minimize Fraud
  • Business Objective – Minimize Defaulters / Minimize Fraud
 

Data Collection

  • Primary Data Sources – Data collected at that moment – Surveys / Experiments
    • Costly
    • Time-consuming / Low quality
    • Get the exact variable
  • Secondary Data Sources – Data which is collected beforehand
    • Quick access to data
    • Free of cost
    • Need not have data of interest
 

Data Cleansing / Data Preparation / Exploratory Data Analysis / Feature Engineering

  • Data Cleansing / Data Preparation
    • Outlier Analysis / Treatment – 3R (Rectify, Retain, Remove)
    • Missingness of data – Imputation – Mean, Median, Mode, Regression, KNN
    • Standardization (X-Min(X)/Range(X) / Normalization (X-Mu/Sigma)) – Unitless and Scale Free
    • Discretization / Binning / Grouping
    • Transformation (log, exp, etc.)
      • Non-linear
      • Non-normal
      • Heteroscedasticity – unequal variance
      • Collinearity
    • Dummy variable creation – One hot encoding
  • Exploratory Data Analysis
    • First-moment business decision / Measures of central tendency
      • Mean, Median, Mode
    • Second-moment business decision / Measures of dispersion
      • Variance, Standard Deviation, Range
    • Third-moment business decision – Skewness
    • Fourth-moment business decision – Kurtosis
    • Graphical Representation
      • Univariate
        • Box Plot
          • Primary purpose – Identify outliers
          • Secondary purpose – Identify shape of distribution
        • Histogram
          • Primary purpose – Identify Shape of distribution
          • Secondary purpose – Identify outliers
        • Q-Q plot – Data are normal or not
      • Bivariate
        • Scatter plot
          • Primary purposes
            • Direction-Positive, Negative, no correlation
            • Strength – Strong, moderate, weak – Subjective; Objective – correlation coefficient; r: -1 to +1; |r| > 0.85; |r| < 0.4
            • Linear or Non-linear / Curvilinear
          • Secondary purposes
            • Scatter plot
              • Primary purposes
                • Clusters
                • Outliers
            • Feature Engineering / Feature Extraction – Using your given variables, try to apply domain knowledge to come up with more meaningful derived variables
            • Feature Selection -> Decision Tree (Information Gain), Random Forest (Variable Importance plot), Hypothesis testing, Lasso regression, Ridge regression
 

Data Mining (Cross-sectional)

  • Supervised Learning / Machine Learning / Predictive Modelling (Y known)
    • Regression Analysis (Interpret the parameters)
      • Y= Continuous -> Linear Regression
      • Y = Discrete (2 categories) -> Logistic Regression
      • Y = Discrete (> 2 categories) -> Multinomial / Ordinal Regression
      • Y = Count -> Poisson / Negative Binomial Regression
      • Excessive Zero – ZIP / ZINB / Hurdle
    • KNN
    • Black Box Techniques (No interpretation exists)
      • Neural Networks
      • SVM
    • Ensemble Techniques
      • Stacking
      • Bagging(Random Forest)
      • Boosting (Decision Tree)
  • Unsupervised Learning (Y unknown)
    • Clustering / Segmentation – Reduce the rows
      • K-Means / non-hierarchical – Upfront determine the # of clusters – Scree plot / Elbow curve
      • Hierarchical / Agglomerative – Dendrogram
      • DBSCAN
      • OPTICS
      • CLARA
      • K-medians / K-Medoids / K-modes
    • Dimension Reduction – Reduce the columns
      • PCA, Factor Analysis
      • SVD
    • Association Rules / Market Basket Analysis / Affinity Analysis
      • Support
      • Confidence
      • Lift Ratio > 1 => Antecedent and Consequent have strong association
    • Recommender Systems
    • Network Analytics
      • Degree
      • Closeness
      • Betweenness
      • Eigenvector
      • Page Rank
    • Text Mining & NLP
      • BoW
      • TDM / DTM
      • TF / TFIDF
  • Forecasting / Time Series
    • Model-Based Approaches
      • Trend
        • Linear
        • Exponential
        • Quadratic
      • Seasonality
        • Additive
        • Multiplicative
    • Data-Based Approaches
      • AR
      • MA
      • ES
        • SES
        • Holts
        • HoltWinters

78% of professionals in Asia Pacific say higher data literacy will enhance their credibility at work.

(*APAC Data Literacy Survey 2018: Qlik)

Block Your Time

data science course in aurangabad- 360digitmg

120 hours

Classroom Sessions

data science course in aurangabad - 360digitmg

80 hours

Assignments &
e-Learning

data science course in aurangabad - 360digitmg

80 hours

Live Projects

Who Should Sign Up?

  • IT Engineers
  • Data and Analytics Manager
  • Business Analysts
  • Data Engineers
  • Banking and Finance Analysts
  • Marketing Managers
  • Supply Chain Professionals
  • HR Managers
  • Math, Science and Commerce Graduates

Tools Covered

data science using python in aurangabad data science using r programming in aurangabad data science using r studio programming in aurangabad

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