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Data Science Training

If you are looking for focus Predictive Modelling data science training, then its Qshore data science training in kondapur is the right place.  Data Science Specialization training in Hyderabad, which covers all data science concepts and how to use software tools like, R, Python, Business intelligence - Market intelligence. Qshore Gachibowli is also known to be a niche analytics training provider, and exclusively training on analytics and data science.

Qshore have the expert certified faculty of analytics, data science, big data experts and courses are all created and curated by them.

How Data Science works: Data science incorporates tools from multi-disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes. The disciplinary areas that make up the data science field include mining, statistics, machine learning, analytics, and some programming. Soo qshore Data science training program in gachibowli is specialized in these areas. Machine learning is an artificial intelligence tool that processes mass quantities of data that a human would be unable to process in a lifetime. Machine learning perfects the decision model presented under predictive analytics by matching the likelihood of an event happening to what actually happened at the predicted time.

 The scope of a data scientist is not only machine learning. The scope is a having experience with a wide variety of methods and a problem-solving mindset. The best way to get those: good mathematics and statistics training where our trainers are expertized in data science training in Hyderabad kondapur.

 

Course features:

  • Exclusive doubt clarification session on every weekend
  • Real-Time Case Study driven approach
  • Placement Assistance

 

Pre-Requisite / Qualification:

Any Graduate. No programming and statistics knowledge or skills required

Duration of the course:

90 Hours (On working a days-one hour and weekends-3hrs).

Mode of course delivery:

Classroom/Online Training

 

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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