Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured similar to data mining. Data science is a “concept to unify statistics, data analysis and their related methods” in order to “understand and analyse actual phenomena” with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the sub-domains of machine learning, classification, cluster analysis, data mining, databases, and visualisation.

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This course will give you a full overview of the Data Science journey. Upon completing this course you will know

  • How to prepare and clean your data for analysis
  • How to perform basic visualization of your data
  • How to model your data
  • How to curve-fit your data And finally, how to present your findings and wow the audience, this course will give you so much practical exercises that real world will seem like a piece of cake when you complete this training.

Who can attend?

IT Beginners/ Fresher’s/ Job Seeker’s/Developers/ Data Analysts

Prerequisite for Data Science Training

No specific prerequisites are required.

Training Highlights

  • Training on latest versions
  • In-depth coverage with real world examples and scenarios
  • Extensive syllabus, compared to any other institute
  • Clear explanation of each concept
  • Top Quality course material for self-study and future reference
  • Interview questions
  • Hard copy of Material
  • Assistance on resume preparation
1. Introduction to Data Science

2. Introduction to Data Analytics

3. Introduction to Predictions

4. R

  • Operators
    • Arithmetic
    • Relational
    • Logical
    • Assignment
    • Miscellaneous
  • Variables
  • Decision Making
    • If
    • If Else
    • Switch
  • Loops
    • Repeat
    • While
    • For
  • Loop Controls
    • Break statement
    • Next Statement
  • Functions
    • Built in Functions
    • User Defined Functions
  • Strings
  • Vector
  • Lists
    • Naming
    • Manipulating
    • Merging
  • Matrices
  • Arrays
  • Factors
  • Data Frames
  • Packages
  • Reading files
    • CSV file
    • Excel Files
    • Data base
  • Plots
    • Bar chart
    • Line Chart
    • Histogram
    • Box plots
    • Scatter plots
5. Python

  • Operators
    • Arithmetic
    • Relational
    • Logical
    • Assignment
    •  Miscellaneous
  • Variables
  • Strings
  • Tuples
  • Lists
  • Dictionary
  • Decision Making
    • If
    • If else
    • Nested statements
  • Loops
    • While
    • For loops
  • Strings
  • Tuples
  • Dictionaries
  • Functions
  • Modules
  • Different packages in python i.e.:
    • numpy
    • scipy
    • scikitetc
  • File I/O
  • Exceptions
4. Statistics

  • Measures of central Tendencies:
    • Mean,
    • Mode,
    • Median
  • Distributions: Discrete, Continuous
  • Skewness
  • Curtosis
  • Central Limit Theorem
  • Coefficient of Variation
  • Hypothesis testing
  • Z Score
  • F score
  • Chi Square
  • ANOVA
  • Probability
  • Conditional Probability
  • Exploring relationships between variables
  • All Regression techniques
    • Simple linear regression
    • Ordinary least squares estimation
    • Correlations
    • Multiple linear regressions
    • Logistic regression
    • Poison regression
  • Exploring the structure of data
  • Exploring numeric variables
  • Exploring categorical variables
  • Exploring relationships between variables
 5. Machine Learning

Unsupervised learning

  • Clustering
  • PCA
  • Density Estimation
  • Item set mining
  • Co-occurrence

Supervised learning

Algorithms

  • KNN
    • Measuring similarity with distance, choosing an appropriate k, preparing data for use with k-NN
  • Naive Bayes
    • Understanding probability
    • Understanding joint probability
    • Computing conditional probability with Bayes’ theorem
    • Classification with Naive Bayes
    • The Laplace estimator
    • Using numeric features with Naive Bayes
  • Decision Tress
    • Choosing the best split
    • Gini Index, Chi Square, Entropy
  • 1R/RIPPER
    • Rules from decision trees
    • What makes trees and rules greedy?
  • Random Forests
    • Neural network
    • From biological to artificial neurons
    • Activation functions
    • Network topology
    • The number of layers
    • The direction of information travel
    • The number of nodes in each layer
    • Training neural networks with back propagation
  •  SVM
    • Classification with hyper planes
    • The case of linearly separable data
    • The case of nonlinearly separable data
    • Using kernels for non-linear spaces
  • Market Basket Analysis
    • The Apriori algorithm for association rule learning
    • Measuring rule interest – support and confidence
    • Building a set of rules with the Apriori principle
Clustering

  • K – Means
  • Clustering as a machine learning task
  • The k-means clustering algorithm
  • Using distance to assign & update clusters
  • Choosing the appropriate no. of clusters
Evaluating Model Performance
Measuring performance for classification

  • Working with classification prediction data in R
  • A closer look at confusion matrices
  • Using confusion matrices to measure performance
  • Beyond accuracy – other measures of performance
  • The kappa statistic
  • Sensitivity and specificity
  • Precision and recall
  • The F-measure
  • Visualizing performance trade-offs
  • ROC curves
Estimating future performance

  • The holdout method
  • Cross-validation
  • Bootstrap sampling
Specialized Machine Learning Topics

  • Working with proprietary files and databases
  • Working with online data and services
  • Improving the performance of R
Time Series forecasting models

  • ARIMA
  • NAÏVE
Improving Model Performance

Tuning stock models for better performance

  • Using caret for automated parameter tuning
  • Creating a simple tuned model
  • Customizing the tuning process
Improving model performance with meta-learning

  • Understanding ensembles
  • Bagging

Boosting

UCLID IT School is the pioneer in Oracle Exadata Training.  At UCLID, you are assured to get pure and clean training on Oracle Exadata, handing by Mr. Muralidhar who is huge corporate experienced in Oracle Exadata. We have mastered the art of teaching Oracle DBA and changed many lives for good.  At UCLID, we follow an easy, simple and no-nonsense approach towards making you a master in Oracle DBA.

With it’s enviable & impeccable track record of training and placing thousands of students as Oracle DBAs in many IT organizations, Uclid has the following unique advantages:

  • Training by former employees of Oracle India and real time IT professionals from various top IT firms
  • Training on Real Time scenarios and case studies
  • Most in-depth and comprehensive training on every topic of the course
  • Well structured Training Material for future reference
  • Training on Latest Versions – Oracle 12c
  • Individual attention and group discussions for Interview preperation
  • Dedicated systems for Lab practice supported by qualified Lab Administrators and UPS for power backup
  • Unlimited Lab Access till you get job
  • 100% Placement Assistance backed by many top IT organizations (around 16 Top IT companies hire fresher DBAs from Uclid)
  • 100% Satisfaction guarenteed

UCLID has best of the best teaching faculties who are real time IT Professionals working with top MNCs. Each faculty has a minimum 10 years of real time experience in Programming and working in the capacity of project leaders and project managers in various MNCs. You will benefit from their rich experience by going through the real time scenarios and case studies during the training. At the end of the course you will not only learn DATA SCIENCE but familiarize yourself with the real time aspects of DATA SCIENCE.