Data Analyst Roles and responsibilities

An Overview of Data Analyst Roles and Responsibilities

A data analyst is responsible for collecting, cleaning and organizing large data sets, and analyzing them to uncover actionable insights that can drive business strategy and decisions.

Some typical data analyst roles and responsibilities include:

• Retrieving data from multiple sources, including databases and software systems
• Data cleaning and preparation – identifying incomplete, incorrect, inaccurate or irrelevant parts of the data
• Conducting quantitative and qualitative data analysis using statistical methods and programming languages like SQL, Python and R
• Applying modeling techniques like machine learning algorithms to data in order to uncover patterns and predictive insights
• Visualizing data findings using data visualization tools like Tableau, Power BI and Excel
• Communicating data-driven insights and recommendations clearly to stakeholders through reports and presentations

 

Must-Have Data Analyst Skills

While statistics, communication and problem-solving abilities are crucial, every aspiring data analyst needs to build up a robust set of technical qualifications.

SQL

SQL (Structured Query Language) is a standard programming language used for managing and analyzing data stored in relational databases. Whether you’re extracting specific data points, identifying relationships between data sets or updating existing data, SQL querying is an indispensable aspect of a data analyst’s day-to-day work. Fluency in SQL also enables effective collaboration with database administrators and data engineers.

Some common SQL skills needed include:

• Writing SELECT statements to retrieve relevant data
• Filtering large data sets with WHERE clauses
• Using aggregate functions like COUNT, SUM, MAX and AVG
• Joining data from multiple related database tables
• Modifying database structures and contents with DDL and DML

SQL querying forms the initial stage of the data analysis process for most data analysts. Mastering this skill is therefore the first step to becoming an expert analyst.

Statistical Programming Languages

While spreadsheet programs like Excel allow you to work with small, single data sheets, statistical programming languages like Python, R and MATLAB provide the firepower for complex quantitative analysis on larger datasets.

Python and R in particular have become ubiquitous in data science and analytics. Learning at least one of them is indispensable for aspiring data analysts. Here’s an overview:

Python

Python is a versatile, beginner-friendly and general-purpose programming language, equipped with specialized libraries and frameworks tailored for machine learning, predictive modeling, data visualization and statistical analysis. Key reasons data analysts should learn Python:

• Open-source, with rich documentation and active community support
• Easy to read and write, enabling faster development
• Integrates seamlessly with Big Data tech like Apache Spark and Hadoop
• Packed with data-focused libraries like NumPy, SciPy, Pandas, Matplotlib and Seaborn

R

Developed specifically for statistical analysis and graphics, R provides an extensive collection of packages and functionalities for advanced analytics. Some notable R features:

• Specialized data structures and data manipulation capabilities
• Inbuilt statistical and graphical capabilities
• Highly extensible with over 17,000 libraries for niche tasks
• Dominates statistical modeling and machine learning applications
• Integrates with Python, SQL and other Big Data tools

Whichever route you take, developing proficiency in using Python or R will dramatically boost your capabilities as a data analyst or data scientist.

Data Visualization

While number crunching and coding form the backbone of data analysis, visualizing data findings is equally important – especially when presenting insights to business teams and stakeholders.

Data analysts rely on data visualization software like Tableau, Power BI, Qlik and D3.js to create interactive dashboards, charts, graphs and other graphics that bring data analysis to life.

Some useful data visualization skills include:

• Transforming raw datasets into formats like charts, graphs and maps
• Conveying relationships between variables through scatter plots and heat maps
• Comparing categorical data visually using pie, donut and bar charts
• Highlighting chronological patterns over time using line graphs
• Simplifying complex numerical figures via reports and summaries

Data presentation is where data analysts can truly provide intelligence and value to an organization. Strong data visualization skills enable you to communicate insights far more effectively to drive business strategy.

 

Categories: codes , CyberSecurity , ITadmins

Blender 4.0 Release Arrives With Game-Changing Rendering Upgrades

 Blender 4.0 release makes it an exciting program for all kinds of graphics professionals and artists.

Blender is a very popular open-source 3D computer graphics suite that has a vast variety of use cases, ranging from the creation of animated films, all the way to motion graphics, visual effects, and more.

With a recent announcement, the Blender Foundation revealed the Blender 4.0 release, that represents “a major leap for rendering, creating tools, and more to take your Freedom to Create to new heights.” So, let's look at what's behind this release.

Being a major release, Blender 4.0 has plenty to offer. However, we will be only taking a look at some of the key highlights:

• User Interface Improvements
• Light and Shadow Linking
• Revamped Principled BSDF
• Better Rigging

While we mention some details as you read on, here is the official video to sum up the major changes:

User Interface Improvements

The most important user-facing thing on Blender, the interface font, has been changed to “Inter”. This move was made in a bid to improve text readability across the application, irrespective of the display size.

Then there's the newly added ability to search for stuff by pressing the space bar on regular dropdown/context menus, and a tweaked splash screen which now makes it easier to carry over saved settings from older Blender installs.

Even a new “Save Incremental” option was added to the File menu that saves the current .blender file with a numerically incremented name.

Light and Shadow Linking

Dubbed by the devs as their most awaited feature, Blender 4.0 features Light and Shadow Linking. With these features, a designer could set lights to affect specific objects in a scene, and even have control over which objects can block light, acting as shadow blockers.

For more details on how these work, you can refer to the official documentation.

Revamped Principled BSDF

Blender's Principled BSDF system has seen a big revamp with the upgrade, it now features support for a larger variety of material types and is more efficient.

Some highlights of the revamp include:

• Coat, it is placed above all the base layers, including the emission layer, to simulate things like a phone display behind a glass surface.
• Sheen, it now uses a new microfiber shading model, and acts as the top layer above emission and coat.
• Implementation of Multiple scattering GGX for more efficient rendering in Cycles, resulting in less power being used while rendering.
• Edge tinting for metallic surfaces, allowing for an artist-friendly way to render complex index of refraction, based on the F82 tint model.

The illustration below should give you a better look at how things are with this revamp of Principled BSDF:

Better Rigging

Blender 4.0 features a dedicated “Bone Collections” for Armature Bones, this implementation is carried over from the existing implementation for objects.

It replaces both the legacy numbered layers and bone groups feature, allowing for the Select Grouped operator to select by bone color/collection, setting bone colors on armature bones, and more.

Even the pose library sees an update with the recently introduced asset shelf that makes pose assets available in the 3D viewport.

🛠️ Other Changes and Improvements

There's more. Some other noteworthy changes include:

• Support for Intel HD4000 series GPUs has been dropped.
• A new blendfile compatibility policy has been implemented.
• Snapping has been improved, allowing for faster and more precise snaps.
• The minimum required OpenGL version for Linux and Windows is now 4.3.
• Filmic has been replaced by AgX view transform for better handling of colors in over-exposed areas.

You may also refer to the official release notes for more details of this Blender release.

📥 Download Blender 4.0

You can get the latest tar package of Blender from the official website for Linux, Windows, and macOS.

But, if you were looking for something different, then you could also get it from the Snap store and Steam.

Categories: 3D-design , Blender , codes , graphics , opensource

10 Best Python Libraries for Machine Learning

Python has grown in popularity over the years to become one of the most popular programming languages for machine learning (ML) and artificial intelligence (AI) tasks. It has replaced many of the existing languages in the industry, and it is more efficient when compared to these mainstream programming languages. On top of all of that, its English-like commands make it accessible to beginners and experts alike. 

Another fundamental feature of Python that draws many of its users is its vast collection of open-source libraries. These libraries can be used by programmers of all experience levels for tasks involving ML and AI, data science, image and data manipulation, and much more. 

Why Python for Machine Learning and AI?
Python’s open-source libraries are not the only feature that make it favorable for machine learning and AI tasks. Python is also highly versatile and flexible, meaning it can also be used alongside other programming languages when needed. Even further, it can operate on nearly all OS and platforms on the market. 

Implementing deep neural networks and machine learning algorithms can be extremely time consuming, but Python offers many packages that cut down on this. It is also an object-oriented programming (OOP) language, which makes it extremely useful for efficient data use and categorization. 

Another factor that makes Python favorable, especially to beginners, is its growing community of users. Since it is one of the fastest growing programming languages in the world, the number of Python developers and development services has exploded. The Python community is growing alongside the language, with active members always looking to use it to tackle new problems in business.

Now that you know why Python is one of the top programming languages, here are the 10 best python libraries for machine learning and AI: 

1. numpy 
2. sciPy 
3. Theano 
4. Pandas 
5. TensorFlow 
6. Hard 
7. Pytorch 
8. sciKit:Learn
9. matplotib 
10. Plotly

NumPy

NumPy is widely regarded as the best Python library for machine learning and AI. It is an open-source numerical library that can be used to perform various mathematical operations on different matrices. NumPy is considered one of the most used scientific libraries, which is why many data scientists rely on it to analyze data. 

NumPy arrays require far less storage area than other Python lists, and they are faster and more convenient to use. You can manipulate the data in the matrix, transpose it, and reshape it with NumPy. All in all, NumPy is a great option to increase the performance of machine learning models without too much complex work required.

Here are some of the main features of NumPy: 

• High-performance N-dimensional array object.
• Shape manipulation.
• Data cleaning/manipulation.
• Statistical operations and linear algebra.

SciPy

SciPy is a free, open-source library that is based on NumPy. It is especially useful for large sets of data, being able to perform scientific and technical computing. SciPy also comes with embedded modules for array optimization and linear algebra, just like NumPy. 

The programming language includes all of NumPy’s functions, but it turns them into user-friendly, scientific tools. It is often used for image manipulation and provides basic processing features for high-level, non-scientific mathematical functions.

SciPy is one of the foundational Python libraries thanks to its role in scientific analysis and engineering.

Here are some of the main features of SciPy:

• User-friendly.
• Data visualization and manipulation.
• Scientific and technical analysis. 
• Computes large data sets. 

 

Theano
A numerical computation Python library, Theano was developed specifically for machine learning. It enables optimization, definition, and evaluation of mathematical expressions and matrix calculations. This allows for the employment of dimensional arrays to construct deep learning models. 

Theano is a highly specific library, and it is mostly used by machine learning and deep learning developers and programmers. It supports integration with NumPy and can be used with a graphics processing unit (GPU) insead of a central processing unit (CPU), which results in data-intensive computations 140 times faster. 

Here are some of the main features of Theano:
·         Built-in validation and unit testing tools.
·         Fast and stable evaluations.
·         Data-intensive calculations.
·         High-performing mathematical computations.

Pandas

Another top Python library on the market is Pandas, which is often used for machine learning. It acts as a data analysis library that analyzes and manipulates data, and it enables developers to easily work with structured multidimensional data and time series concepts. 

The Pandas library offers a fast and efficient way to manage and explore data by providing Series and DataFrames, which represent data efficiently while also manipulating it in different ways. 

Here are some of the main features Pandas:
·         Indexing of data.
·         Data alignment
·         Merging/joining of datasets.
·         Data manipulation and analysis.

 

TensorFlow

Another free and open-source Python library, TensorFlow specializes in differentiable programming. The library consists of a collection of tools and resources that enables beginners and professionals to construct DL and ML models, as well as neural networks.

TensorFlow consists of an architecture and framework that are flexible, enabling it to run on various computational platforms like CPU and GPU. With that said, it performs best when operated on a tensor processing unit (TPU). The Python library is often used to implement reinforcement learning in ML and DL models, and you can directly 

visualize the machine learning models. 

Here are some of the maine features of TensorFlow: 

• Flexible architecture and framework.
• Runs on a variety of computational platforms. 
• Abstraction capabilities
• Manages deep neural networks. 

Keras

Keras is an open-source Python library aimed at the development and evaluation of neural networks within machine learning and deep learning models. It is capable of running on top of Theano and Tensorflow, which means it can train neural networks with little code. 

The Keras library is often preferred due to it being modular, extensible, and flexible. This makes it a user-friendly option for beginners. It can also integrate with objectives, layers, optimizers, and activation functions. Keras operates in various environments and can run on CPUs and GPUs. It also offers one of the widest ranges for data types.

Here are some of the main features of Keras: 
·         Data pooling.
·         Developing neural layers.
·         Builds deep learning and machine learning models.
·         Activation and cost functions.

 

PyTorch
One more option for an open-source machine learning Python library is PyTorch, which is based on Torch, a C programming language framework. PyTorch is a data science library that can be integrated with other Python libraries, such as NumPy. The library can create computational graphs that can be changed while the program is running. It is especially useful for ML and DL applications like natural language processing (NLP) and computer vision.

Some of the main selling points of PyTorch include its high speed of execution, which it can achieve even when handling heavy graphs. It is also a flexible library, capable of operating on simplified processors or CPUs and GPUs. PyTorch has powerful APIs that enable you to expand on the library, as well as a natural language toolkit. 

Here are some of the main features of PyTorch:
·         Statistical distribution and operations.
·         Control over datasets.
·         Development of DL models.
·         Highly flexible. 

 

Scikit-Learn
Originally a third-party extension to the SciPy library, Scikit-learn is now a standalone Python library on Github. It is utilized by big companies like Spotify, and there are many benefits to using it. For one, it is highly useful for classical machine learning algorithms, such as those for spam detection, image recognition, prediction-making, and customer segmentation. 

Another one of the main selling points of Scikit-learn is that it is easily interoperable with other SciPy stack tools. Scikit-learn has a user-friendly and consistent interaction that makes it easy for you to share and use data. 

Here are some of the main features of Scikit-learn:
·         Data classification and modeling.
·         End-to-end machine learning algorithms.
·         Pre-processing of data.
·         Model selection. 

 

Matplotlib
Matplotlib is a unity of NumPy and SciPy, and it was designed to replace the need to use proprietary MATLAB statistical language. The comprehensive, free and open-source library is used to create static, animated, and interactive visualizations in Python. 

The Python library helps you understand the data before moving it to data processing and training for machine learning tasks. It relies on Python GUI toolkits to produce plots and graphs with object-oriented APIs. It also provides an interface similar to MATLAB so a user can carry out similar tasks as MATLAB. 

Here are some of the main features of Matplotlib:
·         Create publication quality plots.
·         Customize visual style and layout.
·         Export to various file formats.
·         Interactive figures that can zoom, pan, and update. 

 

Plotly
Closing out our list of 10 best Python libraries for machine learning and AI is Plotly, which is another free and open-source visualization library. It is highly popular among developers thanks to its high-quality, immersive, and publication-ready charts. Some of the charts that are accessible through Plotly include boxplots, heatmaps, and bubble charts. 

Plotly is one of the best data visualization tools on the market, and it is built on top of the D3.js, HTML, and CSS visualization toolkit. Written in Python, it uses the Django framework and can help create interactive graphs. It works on different data analytics and visualization tools and enables you to easily import data into a chart. You can also use Plotly to create slide decks and dashboards. 

Here are some of the main features of Plotly: 
·         Charts and dashboards.
·         Snapshot engine.
·         Big data for Python.
·         Easily import data into charts. 

 

 

Categories: codes , Python , unix