Author name: cndro

When you say “I hate programming”, you are most likely echoing the voice of many others that feel the same way you do about programming. In this blog post, I am going to tell you 3 ways you can spark your interest and start liking programming. Computer programming  is the process of designing and building an executable computer program for accomplishing a specific computing result. Programming involves tasks such as; analysis, generating algorithms, profiling algorithms’ accuracy and resource consumption. It is also the implementation of algorithms in a chosen programming language (commonly referred to as coding).  There are so many broad concepts in programming that can make the learning process frustrating and build a sense of hatred in our minds. Personally,  coding assisted me in developing several characteristics such as critical thinking, which is quite important to any successful person. The challenges and tasks in coding steadily increased my knowledge and brought fulfillment and excitement into my work life. I know how “boring” coding might seem to some people, some even think or feel they can never understand the dots and points of programming. For you to be reading this article right now means you want to change that feeling. So here are my 3 tips on how I overcame the “not good enough” feeling. Note: This is not a life hack, but tips to overcome your hatred for programming Identify why you hate programming The reason why most people hate coding is that they can’t seem to figure it out. This automatically makes them feel like they hate it. So, ask yourself if you actually hate it, or if you hate the idea of not being able to figure it out. Once you have identified the problem, you are well on your way to solving it and improving your interest in coding. Work on your mindset The hate you have towards coding came from your mind. The moment I realized this, I immediately started working on my psych towards coding. Just like my attitude toward vegetables, I decided to focus on the benefits of becoming a good programmer. This method created a logic in my head that kept m interested in coding Be open to continuous learning Read code written by people because the first law of programming is  “code is read more than written.” There are multiple resources all over the internet that I used to learn, with quality content for little or no cost. In programming, learning never stops. The more you learn, the more there is to know. Fully exploit these resources, and start with easier coding challenges as you steadily grow.  Program Yes, that’s right. The best way to enjoy coding is to actually code. As you explore several learning resources, look out for challenges. Try these challenges out yourself, till you get them. Trust me, there is a satisfying feeling when you see your codes actually run. These tips really helped me, improved my interest in coding and made me a better programmer. You can adopt them too. Leave comments below on how you think these tips would help and good luck.

Transitioning or switching to a career in data analysis is not a piece of cake, especially if your current career path is non-technical. It is a road paved with many obstacles and you can get overwhelmed and frustrated somewhere along the line. In this blog post, I’m going to discuss a few things that will help you to switch easily to a career in data analysis. I remember when I wanted to switch careers too. I had a long list of courses to take on several learning platforms. In the end, I became overwhelmed and had to retrace many steps back to do it the right way. So, it is completely okay that you are looking online for help on how to switch to a career in data analysis. Note: This is not a life hack. They are just tips that will go a long way in easing your career transition. Step 1: Is transitioning necessary? As much as the world is now data-inclined, a career in data analysis is really not for everybody. Before transitioning, be sure to ask yourself if this switch is necessary. Do you really want to do it? If you are only transitioning because everyone is doing it, then you might not be able to push through when the challenges come. Step 2: Research your position of interest. There are many job positions in data analysis with different sets of required skills. While the title of a data analyst is the most popular one, and most likely the first one you thought of, there are other options. You can be a database administrator, a BI analyst, an IT systems analyst, a healthcare analyst, an operations analyst, etc. Each of these job positions has their required skill sets. Think about the job position you are interested in, and find out the necessary skill sets. Research industries that will require such services and be sure to check how rewarding it is. Step 3: Develop your skills. There are certain skills that are required of almost every data analyst. These skill sets are important when switching to a career in data analysis, and they include: Creative and Analytical thinking: It is critically important to be able to think through problems with a curious and creative point of view. I mean, how can you properly analyze data without good analytical skills? Data visualization: This is an extremely important skill for every data analyst. There are tools specifically built for data visualization, and the best way to fully equip yourself with this skill is to master these tools. A good example is Tableau. Being an expert in Tableau takes your skill level as a data analyst from 0 to 100 real quick. It also increases your chances of getting a job fast. There are many Tableau training programs but I will recommend Cndro’s Tableau training because it focuses on practicality and prepare you for actual on-the-job challenges. Programming languages: One of the most essential skills to effectively switch to a career in data analysis is the ability to read and write in code. Today’s most in-demand analytical languages are R and Python Advanced Microsoft Excel: SQL Databases: SQL databases are relational databases with structured data. Data is stored in tables and a data analyst pulls information from different tables to perform analysis. The ability to do this effectively will further ease your transitioning Data Cleaning: When data isn’t neatly stored in a database, data analysts must use other tools to gather unstructured data. Once they have enough data, they clean using programming. There are many other skills, but their requirement levels can vary depending on the job position. Step 4: Create a Portfolio While switching to a career in data analysis, it is important to create a portfolio. Employers want to see concrete evidence of the things you can do, so start building a portfolio the moment you begin your learning process. As you develop your skills, update your portfolio. Not sure where to start? You can create a GitHub account, or sign up on Kaggle to have access to many open projects that you can practicalize with. These projects will strengthen your portfolio. Step 5: Build a network. It doesn’t matter if you love it or hate it, networking is important if you want to take that giant leap and switch to a career in data analysis. There are different ways to expand your network without going to networking events. You can start by getting the word out about your interest in data analysis to family, friends, and anyone who cares to listen. Post about it on LinkedIn, and connect with people in your field of interest (professionals and amateurs). You can find like-minded people on platforms like Quora and reach out to them for connections, advice, etc. Building and expanding your network will completely ease your transition process to a career in data analysis. Transitioning to data analysis There is no magical process to this, transitioning careers don’t happen overnight, especially to data analysis. You have to be willing to put in the work and develop yourself. If you have started your transitioning process or you are just about to, you can ask questions in the comments section for more help.

There are different methods used in communicating with the Tableau Server, but one of the outstanding methods is through the Tableau Server Client (TSC). The tableau server is one of the tools in the tableau software product suite. You can read about tableau and it’s tools here. In this article, I am going to share with you what TSC is and how important it is to any Tableau developer What is TSC? Tableau Server Client (TSC) is a python library for the Tableau Server REST API. You can do almost everything on the Tableau Server with the REST API. You can create users, create groups, query projects, query sites to retrieve data sources and workbooks, you can do so much more on the Tableau Server just with the Tableau Server Client. One of the strengths of this python library (TSC), is that you can query multiple data sources and workbooks at a time. TSC allows you to communicate with the tableau server using python programming language, giving you more control over the server. Downloading datasources from Tableau server using TSC. Downloading data sources is an inevitable task for every tableau developer. Since this task is a very common one, I am going to show you how to carry it out effectively using TSC. I’m going to do a quick walkthrough of how to download all the datasources that has been published to the Tableau server default site. For this purpose of this article, I’m going to use the Jupyter notebook. You can also use your favorite IDE to carry out this process. This process is the same for all versions of the tableau server, but I used version 10.5.3. So, let’s jump right in: Step 1: Launch Jupyter notebook. Install TSC using the python line below: !pip install tableauserverclient Step 2: Once the library has been installed, authenticate the connection to the TSC so that Tableau server can identify you as a user. tableau_auth = TSC.TableauAuth(‘USERNAME’, ‘PASSWORD’, ‘SITENAME’) In your case, input your username and password in the respective fields, and “default” as the sitename, because we are trying to download datasources from the default site. If it was from a different site, you will input the name of the site instead. Step 3: After the authentication stage, declare your server to establish a connection between TSC and the server. This can be done with this line of code: server = TSC.Server(‘Your Tableau Server Public IP’) Step 4: Get all the datasources in the default site using this line of code: with server.auth.sign_in(tableau_auth):     all_datasources, pagination_item = server.datasources.get() Step 5: Get all the IDs of the datasources so you can use it in referencing the operations that you want to perform on the site. The code for this step is:     datasource_ids = [datasource.id for datasource in all_datasources]  Step 6: Print the total number of datasources in the default site. Then, print the number of the datasources to be downloaded from the server 10.5.3   print(“\nThere are {} datasources on site “.format(len(all_datasources)))    print(“\nThere are {} datasources to be downloaded on site:  “.format(pagination_item.total_available)) Step 7: Loop through the datasources using their ids and download the datasources to your local machine.     for id in datasource_ids:          if file_download.lower().endswith(‘.tdsx’):          file_download = server.datasources.download(id)         print(file_download)   Step 9: Finally, sign out of the server so your information on it is saved. server.auth.sign_out()  Your Jupyter notebook should look like this image below:   How to download datasources from the tableau server default site. Importance of Tableau Server Client to a Tableau Developer As a Tableau developer, having full understanding and control of the Tableau server is of extreme importance. Tableau Server administration is a required skill in the 21st century as a Tableau Developer. TSC helps you to administer your tableau server easily. It makes you able to send instructions to your server from your local computer, publish datasources and workbooks to the server from your local machine.

Most times in Python, you get to import just one file using pandas by pd.read(filename) or using the default open() and read() function in. But news flash, you can actually do more!! In this article, I am going to show you how to import multiple files into your Python IDE. Please note that the IDE I used for this process is Jupyter notebook. Pandas can be used to read certain file types as specified in jupyter notebook. These file types include: 1. clipboard 2. Csv 3. excel 4. Feather 5. Fwf 6. Gbq 7. Hdf 8. Html 9. Json 10. Msgpack 11. Parquet 12. Sas 13. Sql 14. Sql query 15. Sql table 16. Stata 17. Table To see this list in your jupyter notebook. This is all you have to do. 1. Be sure you have pandas installed Pip install pandas 2. Import pandas into your jupyter notebook Import pandas as pd 3. Try to read your file and check for other file formats that can be read in python Data = pd.read_#fileformat(filename) (#fileformat is just a place holder for the file format) After the underscore(_) press the tab key on your keyboard. Importing multiple files in Python Importing multiple files in python is done with a module called GLOB Glob is a module that helps to import any file format into python notebook. It is used with several wildcards to import specific file types to prevent import unnecessary files not needed in your python notebook. To get glob installed, you have to run a pip command in your command prompt or Anaconda Prompt Pip install glob3 Importing glob into python (Anaconda) Import glob Importing all the file in your current directory Myfiles = [I for in glob.glob(‘*’)] Note: * is a wildcard which denotes all. This takes all the files in that current directory into python. Importing all excel file formats in Python Myfiles = [I for in glob.glob(‘*.xlsx’)] The above code can also be written in the default way as shown below: Myfiles = [] (This is an empty list) Creating the for loop For each_file in glob.glob(‘*.xlsx’): Myfiles.append(each_file) Print(Myfiles) This will give you all the excel files in your current directory. On occasions where there are fields like data1.xlsx, data2.xlsx, data3.xlsx, data21.xlsx, data22.xlsx, e.t.c; we can use a wildcard (?) to pick certain files. Mynewfiles = [] For each_file in glob.glob(‘data?.xlsx’): Mynewfiles.append(each_file) Print(Mynewfiles) The code above will give us files with the name data1.xlsx, data2.xlsx, data3.xlsx without data21.xlsx, data22.xlsx even though it is an excel format (.xlsx) Import files with a range of numbers Mynewfiles2 = [] (This is an empty list) For each_file in glob.glob(‘data[0-9].xlsx’): Mynewfiles2.append(each_file) Print(Mynewfiles2) This will return files with the numerical values in the specified location in the specified range. Placeholders Using placeholders can be fun just to make your codes more readable and understandable. Combining {} and .format helps in achieving this. Importing only csv files csv_files = [] for each_file in glob.glob(‘*.{}’.format(‘csv’)): csv_files.append(each_file) print(csv_files) Most times, it is preferred to have your file format assigned to a variable. Importing an XML file Fileformat = ‘xml’ xml_files = [] for each_file in glob.glob(‘*.{}’.format(fileformat)): xml_files.append(each_file) print(xml_files) You can import any file format into python using the above method python. To read these files, you can use the open and read functions in python as seen below: for each_data in xml_files: print(open(each_data, ‘r’)) print(open(each_data, ‘r’).read()) This would return the contents of the file (the xml file).

In Tableau, strings or string data, are data made of text. String functions exist to help you manipulate your string data. There about twenty string functions in Tableau. In this article, I am going to introduce you to ten (10) important string functions in Tableau, and how to use them. You might be wondering “why are string functions so important?” Let’s assume a scenario where you want to pull the first name of all your customers into a new field; a string function will handle that task for you. String Functions In Tableau Important String Functions in Tableau Please note that all the string functions in Tableau are of extreme importance. This article is just exploring 10 of them, and they include: ASCII CONTAINS FIND LEFT LEN MID REGEXP_MATCH REGEXP_REPLACE SPLIT TRIM 1. ASCII This returns the ASCII code value of the first character in a string ASCII ( String ) ASCII (“authors”) = 97 2. CONTAINS This returns a TRUE if the specified substring is present in the given string. CONTAINS ( String, Substring ) CONTAINS (“Calculation” , “alcu”) 3. FIND This returns the position of the specified substring within the given string and returns 0 if the substring is not found. FIND (String, Substring, [ Start ]) The first character in the string is position 1 (not a 0 index). FIND (“Calculation”, “alcu”) = 2 If the start argument is defined, any instance of the substring that appears before the start position are ignored FIND (“Calculation”, “a”, 3 ) = 7 4. LEFT LEFT ( String, num_chars) This returns the specified number of characters from the start of the given string. LEFT (“Calculation”, 5 ) = “Calcu” 5. LEN LEN (String) This returns the number of characters in the given string. LEN (“Calculation”) = 11 6. MID MID (String, Start, [length]) This returns the characters from the middle of a text string given a starting position and a length. The first character in the string is in position 1. If the length is not included, all characters to the end of the strings are not returned. MID (“Tableau Software”,  9 ) = “Software” If the length is included, up to that many characters are returned. MID (“Tableau Software”, 2, 4) = “able” 7. REGEXP_MATCH REGEXP_MATCH ( String , Pattern ) This returns true if a substring of the provided string matches the regular expression pattern. REGEXP_MATCH ( ‘ – ( [1234] . [ The.Market] ) -’ ,  ‘\ [\s*(w*\.)  (  \w*\s*\]) ‘ ) = true 8. REGEXP_REPLACE REGEXP_REPLACE ( String, Pattern, replacement) This returns a copy of a given string where the matching pattern is substituted with the replacement string. REGEXP_REPLACE ( ‘abc123’, ‘\s’, ‘-’ ) = ‘abc–123’ 9. SPLIT SPLIT ( String , delimiter, token number) This returns a substring from a string as determined by a delimiter (a separator) extracting the characters from the beginning or end of the string. SPLIT (‘a/b/c/d’ , ‘ / ‘ , 2 ) = ‘b’ A negative token number can also be used. SPLIT (‘a/b/c/d’ , ‘ / ‘ ,  -2 ) =  ‘c’ 10. TRIM TRIM (String) This returns the string with both leading (LTRIM is used for this only ) and trailing (RTRIM is used for this only ) whitespaces removed. TRIM (“  Budget   ”)  =  “Budget” To learn more about all the string functions in tableau, click here.

Salesforce is a CRM which stands for Customer Relationship Management.  In very simple terms, it is an online tool that helps businesses manage their customer information (basically a database of customers) efficiently. Salesforce is a software for every organization, and in this article, I’m going to tell you why. Salesforce organizes data into objects and records just like in an excel spreadsheet where objects are like tabs and a record is like a single row of data. It can also be a unified place to do stuff with the company’s information stored on the secured cloud which makes data accessible anywhere and anytime in the world. Salesforce: A must-have Here are some of the reasons why the software is a must-have for every organization: Flexibility in capacity One of the most significant unique selling points of the Salesforce platform is its high degree of adaptability. The objects to be found in Salesforce can be set entirely in line with your desires at any time. As a user, you are not tied into certain set page layouts, workflows, and processes, and this makes Salesforce’s ecosystem more flexible than other similar systems on the market. Security Salesforce has some security basics that keep your data secured by authenticating users, giving only users access to the data in the company, also sharing objects and fields while monitoring the organization’s security. Countless options with various apps In addition to the clouds we have designed ourselves, Salesforce’s ecosystem also comprises applications you can purchase through the App Exchange. A major benefit compared to other suppliers, where you must settle for integrated tooling from the same supplier. The AppExchange features all kinds of apps capable of supporting your processes (recruitment, sales, marketing, finance, etc.). It is often the case that these apps have been developed by experts in the relevant fields. Consider Data loader (data import), Mailchimp (e-mail marketing), Grow promoter (NPS research) and Ebsta (integration with Gmail) for example.  Lead Management Salesforce.com can help your company track your leads from clicks, that is from being a prospect to closing the deal, while continually optimizing your campaigns across every channel. Make smarter decisions about where to invest your marketing money, thereby reducing financial loss in your company. Reports Our CRM analytics software keeps you updated with customized sales forecasting reports that you can build with ease. Just drag and drop the fields, filters, groupings, and charts that you want, and get an immediate real-time view. Salesforce can generate 1000+ reports in less than a second with just a few clicks on your data which can save your company from quite several stress and time wastage. Contact Management You can have a complete view of your customers, including activity history, key contacts, customer communications, and internal account discussions. Gain insights from popular social media sites such as Facebook, Twitter, LinkedIn, and YouTube all right within Salesforce. Get an accurate view of your entire business with comprehensive forecasts. See a complete view of your entire pipeline and your business, and act where necessary. Provide rapid updates to help management make decisions. Then easily apply your judgment to forecasted amounts at the rep, period, and summary levels. You can also view details about any previous adjustments that you or your team provided while leaving the underlying opportunity data intact.