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Data Science Training

 what is data science?

Data science is a multidisciplinary blend of data inference, algorithm and technology in order to solve analytically complex problems. At the core is data. Troves of raw information, ,streaming in and stored in enterprise data warehouses.development

 

why you want to learn Data science course?

The number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings by 2020 - Forbes Businesses analysing data will see $430 billion in productivity benefits over their rivals not analysing data by 2020

 

About Data Science Training Course

This is a complete Data Science bootcamp specialization training course from qshore technologies that provides you detailed learning in data science,data analytics, project life cycle, data acquisition, analysis, statistical methods and machine learning.You will gain expertise to deploy Recommenders using R programming, data analysis, data transformation, experimentation and evaluation.

 

What you will learn in this Data Science Course?

*Data Science introduction
 *Data acquisition and Data Science lifecycle
 *Experimentation, evaluation and project deployment tools
 *Different algorithms used in Machine Learning
 *Predictive analytics, segmentation using clustering
 *Big Data fundamentals and Hadoop integration with R
 *Deploying recommender systemsonimportancereal world data sets
 *Work on data mining, data structures, data manipulation.
 *Data Scientist roles and responsibilities.and

 

Who should take this Data Science Course? 

Those looking to take up the roles of Data Scientist and Machine Learning Expert
statisticians, developers looking to master machine learning and predictive analytics,
Big data, business intelligence and business analyst professionals, information architects,Everyone can learn data science, its that you need to have interest in stats and topics related to it ...

 

what we provide in our training?

Keynotes During Training
Real Time Project Studies
Soft copy materials
Flexible Timings
Affordable Cost
Trainer Support
Technical Support
Certificate of Completion

 

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Rating:4.9

Votes:1000

Review:I strongly recommend people to go for Devops with Qshore.

  

Curriculum

Here you can download the course and schedule for Data Science Training Download PDF

      • What is Data Science? – Introduction.
      • What background is required?
      • Why Data Science?
      • Importance of Data Science.
      • Demand for Data Science Professional.
      • Brief Introduction to Big data and Data Analytics.
      • Lifecycle of data science.
      • Tools and Technologies used in data Science.
      • What is Machine Learning?
      • Different types of Data Science Tasks.

        • Descriptive statistics and Inferential Statistics
        • Sample and Population
        • Variables and Data types
        • Percentiles
        • Measures of Central Tendency
        • Measures of Spread
        • Skeweness, Kurtosis
        • Degrees of freedom
        • Variance, Covariance, Correlation
        • Standardization/Scaling
        • Probability
        • Expected of ‘x’
        • Sampling Distribution
        • Standard Probability Distribution Functions
        • Bernoulli, Binomial, Normal distributions
        • Standard Normal Deviate
        • Decision Making Rules
        • Test of Hypothesis
        • One sample t-Test, Chi-square
        • Two sample t-Test Analysis of Variance (ANOVA)

          • Summary Statistics
          • Data Transformations
          • Outlier Detection and Management
          • Charts and Graphs
          • One Dimensional Chart
          • Box plots
          • Bar graph
          • Histogram
          • Scatter plots
          • Multi-Dimensional Charts
          • Fancy Charts - Bubble charts

            • Summary Statistics
            • Data Transformations
            • Outlier Detection and Management
            • Charts and Graphs
            • One Dimensional Chart
            • Box plots
            • Bar graph
            • Histogram
            • Scatter Plots
            • Multi-Dimensional Charts
            • Fancy Charts - Bubble charts

              ? Simple Linear Regression
              ? Multiple Linear Regression
              ? Estimation of Model Parameters
              ? Hypothesis Testing in Multiple Linear Regression
              ? Extra sum of squares
              ? R – Square, R- Square Adjusted
              ? Variable Selection
              a. All Possible Regressions
              b. Sequential Selection (Forward, Backward, Stepwise)
              ? Multicollinearity – VIF
              ? Residual Analysis/Regression Diagnostics.
              ? Polynomial Regression
              ? Transformations
              a. Bulging Rules
              b. Box Tidwell
              c. Box cox
              d. Weighted Least Square
              ? Dummy variables
              a. General Concepts of Indicator variables.
              ? Predicted Error sum of squares (PRESS)
              ? Assessing Performance
              a. Variance Biased Trade-off
              b. Resampling Methods
              c. Cross Validation
              d. Leave one out Cross validation
              e. k-Fold Cross Validation
              f. Bootstrap
              ? Logistic Regression
              A Case Study will be presented on Logistic Regression

                Introduction to Supervised and unsupervised Learning
                ? Neural Networks
                a. Network Topology
                b. Single Layer Perceptron
                c. Multi-Layer perceptron
                d. Feed forward and Back propagation Models
                ? Introduction to Deep Learning
                ? Association Rules
                a. Market Basket Analysis
                b. APRIORI
                c. Support, Lift, Confidence
                ? Nearest-Neighbour Methods (KNN – Classifier)
                a. Euclidian Distance
                b. Hamming Distance
                ? Decision Tree
                a. Finding Root Node, Intermediate Nodes, Terminal Nodes
                b. Construction of Rules
                c. Miss classification
                d. Gini Index
                e. Overfitting and Prunning
                f. Regression Trees
                ? Boosting, Bagging and Random Forest
                a. Resampling Methods
                b. Resampling methods with Replacement
                c. Resampling methods without Replacement
                d. Random Forest
                ? Dimensional Reduction Techniques
                1. Principle Component Analysis
                a. Eigen values and Eigen Vectors
                2. Cluster Analysis
                a. Hierarchal Clustering
                b. Linkage Methods
                c. Non- Hierarchal Clustering
                d. K-Means Clustering
                ? Text Mining / Natural Language processing
                a. Unstructured Data
                b. Text Analytics
                c. Cleaning Text data
                d. Tokenization
                e. Pre-processing
                f. Word counts and word clouds
                g. Sentiment Analysis
                h. Text classification
                i. Distance measures
                ? Introduction to probabilistic methods Introduction
                a. Naive Bayes
                b. Joint and Condition probabilities
                c. Classification using Naive Bayes Approach
                ? Support Vector Machines
                a. Maximum Margin Classifier
                b. Support vector Classifier
                c. Support vector machines
                d. Kernels – Linear and Non-Linear

                  ? PYTHON - PROGRAMMING
                  • How to install python (Anaconda)
                  • How to install sciKit Learn (Anaconda)
                  • How to work with Jupyter Notebook
                  • How to work with Spyder IDE
                  • Strings
                  • Lists
                  • Tuples
                  • Sets
                  • Dictionaries
                  • Control Flows
                  • Functions
                  • Formal/Positional/Keyword arguments
                  • Predefined functions (range, len, enumerates etc…)
                  • Data Frames
                  • Packages required for data Science in Python
                  • Lab/Coding

                    • One-dimensional Array
                    • Two-dimensional Array
                    • Pr-defined functions (arrange, reshape, zeros, ones, empty)
                    • Basic Matrix operations
                    • Scalar addition, subtraction, multiplication, division
                    • Matrix addition, subtraction, multiplication, division and transpose
                    • Slicing
                    • Indexing
                    • Looping
                    • Shape Manipulation
                    • Stacking

                      • Series

                      • DataFrame

                      • df.GroupBy

                      • df.crosstab

                      • df.apply

                      • df.map

                        What is Spark
                        Introduction to Spark RDD
                        Introduction to Spark SQL and Data frames
                        Using R-Spark for machine learning
                        Hands-on:
                        installation and configuration of Spark
                        Hands-on Spark RDD programming
                        Hands on of Spark SQL
                        Dataframe programming
                        Using R-Spark for machine learning programming

                          1. Getting R
                          1.1 Downloading R
                          1.2 R Version
                          1.3 32-bit versus 64-bit
                          1.4 Installing
                          2. The R Environment
                          2.1 Command Line Interface
                          2.2 RStudio
                          3. R Packages
                          3.1 Installing Packages
                          3.2 Loading Packages
                          4. Reading Data into R
                          4.1 Reading CSVs
                          4.2 Excel Data
                          4.3 Clipboard
                          5. Advanced Data Structures
                          5.1 Data.frames
                          5.2 Lists
                          5.3 Matrices
                          5.4 Arrays
                          5.5. Factors
                          6. Basics of R
                          6.1 Basic Math
                          6.2 Variables
                          6.3 Data Types
                          6.4 Vectors
                          6.5 Calling Functions
                          6.6 Function Documentation
                          6.7 Missing Data
                          7. Control Statements
                          7.1 if and else
                          7.2 switch
                          7.3 if else
                          8. Loops
                          8.1 for Loops

                          8.2 while Loops
                          8.3 Controlling Loops
                          9. Group Manipulation
                          9.1 Apply Family
                          9.2 aggregate
                          10. Data Reshaping
                          10.1 cbind and rbind
                          10.2 Joins
                          10.3 Reshape2
                          11. String Theory
                          11.1 paste
                          11.2 sprintf
                          11.3 Extracting Text/ Regular Expressions
                          12. Graphs with R and GGPlot2
                          12.1 Basic and Interactive Plots
                          12.2 Dendrograms
                          12.3 Pie Chart and Its Alternatives
                          12.4 Adding the Third Dimension
                          12.5 Visualizing Continuous Data
                          13. Basic Statistics
                          13.1 Summary Statistics
                          13.2 Correlation and Covariance
                          13.3 T-Tests
                          13.4 ANOVA
                          14. Probability Distributions
                          14.1 Normal Distribution
                          14.2 Binomial Distribution

                        About Instructors

                        Mr.Advaith

                        Mr.Advaith
                        Offline Course
                        Duration : 90 Hours
                        Material : Yes
                        Live Project : Yes
                        Software : Yes
                        3000 Students Enrolled
                        Course Completion Certificate

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